Physiological Measurement - IOPscience

Physiological Measurement - IOPscience
Physiological Measurement
The aim of the
Institute of Physics and Engineering in Medicine (IPEM)
is to promote the advancement of physics and engineering applied to medicine and biology for the public benefit. Its members are professionals working in healthcare, education, industry and research.
IPEM publishes scientific journals and books and organises conferences to disseminate knowledge and support members in their development. It sets and advises on standards for the practice, education and training of scientists and engineers working in healthcare to secure an effective and appropriate workforce.
Browse all IPEM journals and ebooks
Physiological Measurement
publishes research on sensing, assessing, visualising, modelling, and controlling physiological functions towards translational applications in clinical research and practice. The journal emphasises the development of cutting-edge methods of measurement utilising artificial intelligence, machine learning, and the large-scale validation of existing techniques.
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The following article is
Open access
The 2023 wearable photoplethysmography roadmap
Peter H Charlton
et al
2023
Physiol. Meas.
44
111001
View article
, The 2023 wearable photoplethysmography roadmap
PDF
, The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
The following article is
Open access
Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG
Hannu Kinnunen
et al
2020
Physiol. Meas.
41
04NT01
View article
, Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG
PDF
, Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG
Objective
: To validate the accuracy of the Oura ring in the quantification of resting heart rate (HR) and heart rate variability (HRV).
Background
: Wearable devices have become comfortable, lightweight, and technologically advanced for assessing health behavior. As an example, the novel Oura ring integrates daily physical activity and nocturnal cardiovascular measurements. Ring users can follow their autonomic nervous system responses to their daily behavior based on nightly changes in HR and HRV, and adjust their behavior accordingly after self-reflection. As wearable photoplethysmogram (PPG) can be disrupted by several confounding influences, it is crucial to demonstrate the accuracy of ring measurements.
Approach
: Nocturnal HR and HRV were assessed in 49 adults with simultaneous measurements from the Oura ring and the gold standard ECG measurement. Female and male participants with a wide age range (15–72 years) and physical activity status were included. Regression analysis between ECG and the ring outcomes was performed.
Main results
: Very high agreement between the ring and ECG was observed for nightly average HR and HRV (r
= 0.996 and 0.980, respectively) with a mean bias of −0.63 bpm and −1.2 ms. High agreement was also observed across 5 min segments within individual nights in (r
= 0.869 ± 0.098 and 0.765 ± 0.178 in HR and HRV, respectively).
Significance
: Present findings indicate high validity of the Oura ring in the assessment of nocturnal HR and HRV in healthy adults. The results show the utility of this miniaturised device as a lifestyle management tool in long-term settings. High quality PPG signal results prompt future studies utilizing ring PPG towards clinically relevant health outcomes.
The following article is
Open access
Physics-informed neural networks for physiological signal processing and modeling: a narrative review
Anni Zhao
et al
2025
Physiol. Meas.
46
07TR02
View article
, Physics-informed neural networks for physiological signal processing and modeling: a narrative review
PDF
, Physics-informed neural networks for physiological signal processing and modeling: a narrative review
Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretability. Like many other fields in engineering and science, the analysis of physiological signals has been influenced by PINNs in recent years. This manuscript provides a comprehensive overview of PINNs from various perspectives in the physiological signal analysis domain. After exploring the literature and screening the search results, more than 40 key studies in the related domain are selected and categorized based on both practically and theoretically significant perspectives, including input data types, applications, physics-informed models, and neural network architectures. While the advantages of PINNs in tackling forward and inverse problems in physiological signal contexts are highlighted, challenges such as noisy inputs, computational complexity, loss function types, and overall model configuration are discussed, providing insights into future research directions and improvements. This work can serve as a guiding resource for researchers exploring PINNs in biomedical and physiological signal processing, paving the way for more precise, data-efficient, and clinically relevant solutions.
The following article is
Open access
An introduction into autonomic nervous function
John M Karemaker 2017
Physiol. Meas.
38
R89
View article
, An introduction into autonomic nervous function
PDF
, An introduction into autonomic nervous function
The results of many medical measurements are directly or indirectly influenced by the autonomic nervous system (ANS). For example pupil size or heart rate may demonstrate striking moment-to-moment variability. This review intends to elucidate the physiology behind this seemingly unpredictable system.
The review is split up into: 1. The peripheral ANS, parallel innervation by the sympathetic and parasympathetic branches, their transmitters and co-transmitters. It treats questions like the supposed sympatho/vagal balance, organization in plexuses and the ‘little brains’ that are active like in the enteric system or around the heart. Part 2 treats ANS-function in some (example-) organs in more detail: the eye, the heart, blood vessels, lungs, respiration and cardiorespiratory coupling. Part 3 poses the question of who is directing what? Is the ANS a strictly top-down directed system or is its organization bottom-up? Finally, it is concluded that the ‘noisy numbers’ in medical measurements, caused by ANS variability, are part and parcel of how the system works. This topical review is a one-man’s undertaking and may possibly give a biased view. The author has explicitly indicated in the text where his views are not (yet) supported by facts, hoping to provoke discussion and instigate new research.
The following article is
Open access
A review of the effect of skin pigmentation on pulse oximeter accuracy
Raghda Al-Halawani
et al
2023
Physiol. Meas.
44
05TR01
View article
, A review of the effect of skin pigmentation on pulse oximeter accuracy
PDF
, A review of the effect of skin pigmentation on pulse oximeter accuracy
Objective
. Pulse oximetry is a non-invasive optical technique used to measure arterial oxygen saturation (SpO
) in a variety of clinical settings and scenarios. Despite being one the most significant technological advances in health monitoring over the last few decades, there have been reports on its various limitations. Recently due to the Covid-19 pandemic, questions about pulse oximeter technology and its accuracy when used in people with different skin pigmentation have resurfaced, and are to be addressed.
Approach
. This review presents an introduction to the technique of pulse oximetry including its basic principle of operation, technology, and limitations, with a more in depth focus on skin pigmentation. Relevant literature relating to the performance and accuracy of pulse oximeters in populations with different skin pigmentation are evaluated.
Main Results
. The majority of the evidence suggests that the accuracy of pulse oximetry differs in subjects of different skin pigmentations to a level that requires particular attention, with decreased accuracy in patients with dark skin.
Significance
. Some recommendations, both from the literature and contributions from the authors, suggest how future work could address these inaccuracies to potentially improve clinical outcomes. These include the objective quantification of skin pigmentation to replace currently used qualitative methods, and computational modelling for predicting calibration algorithms based on skin colour.
The following article is
Open access
pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis
Márton Á Goda
et al
2024
Physiol. Meas.
45
045001
View article
, pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis
PDF
, pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis
Objective.
Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.
Approach.
This work describes the creation of a standard Python toolbox, denoted
pyPPG
, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.
Main results.
The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.
Significance.
Based on these fiducial points,
pyPPG
engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using
pyPPG
can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.
pyPPG
is available on
The following article is
Open access
Measuring psychosocial stress with heart rate variability-based methods in different health and age groups
Santtu M Seipäjärvi
et al
2022
Physiol. Meas.
43
055002
View article
, Measuring psychosocial stress with heart rate variability-based methods in different health and age groups
PDF
, Measuring psychosocial stress with heart rate variability-based methods in different health and age groups
Objective.
Autonomic nervous system function and thereby bodily stress and recovery reactions may be assessed by wearable devices measuring heart rate (HR) and its variability (HRV). So far, the validity of HRV-based stress assessments has been mainly studied in healthy populations. In this study, we determined how psychosocial stress affects physiological and psychological stress responses in both young (18–30 years) and middle-aged (45–64 years) healthy individuals as well as in patients with arterial hypertension and/or either prior evidence of prediabetes or type 2 diabetes. We also studied how an HRV-based stress index (Relax-Stress Intensity, RSI) relates to perceived stress (PS) and cortisol (CRT) responses during psychosocial stress.
Approach.
A total of 197 participants were divided into three groups: (1) healthy young (HY,
= 63), (2) healthy middle-aged (HM,
= 61) and (3) patients with cardiometabolic risk factors (Pts,
= 73, 32–65 years). The participants underwent a group version of Trier Social Stress Test (TSST-G). HR, HRV (quantified as root mean square of successive differences of R–R intervals, RMSSD), RSI, PS, and salivary CRT were measured regularly during TSST-G and a subsequent recovery period.
Main results.
All groups showed significant stress reactions during TSST-G as indicated by significant responses of HR, RMSSD, RSI, PS, and salivary CRT. Between-group differences were also observed in all measures. Correlation and regression analyses implied RSI being the strongest predictor of CRT response, while HR was more closely associated with PS.
Significance.
The HRV-based stress index mirrors responses of CRT, which is an independent marker for physiological stress, around TSST-G. Thus, the HRV-based stress index may be used to quantify physiological responses to psychosocial stress across various health and age groups.
The following article is
Open access
Recent development of respiratory rate measurement technologies
Haipeng Liu
et al
2019
Physiol. Meas.
40
07TR01
View article
, Recent development of respiratory rate measurement technologies
PDF
, Recent development of respiratory rate measurement technologies
Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.
The following article is
Open access
Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
Rakibul Hasan
et al
2026
Physiol. Meas.
47
035014
View article
, Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
PDF
, Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
Objective.
Autoregulation-guided physiological targeting, using metrics such as optimal cerebral perfusion pressure (CPPopt), optimal mean arterial pressure (MAPopt), and optimal bispectral index (BISopt), has emerged as a promising strategy for improving patient outcomes in critical care and neuromonitoring. These targets, derived from the continuous assessment of cerebrovascular reactivity (CVR) indices, are increasingly being studied for their potential to individualize patient management. This review aimed to identify and characterize existing literature detailing the derivation algorithms of CPPopt, MAPopt, and BISopt, focusing on key computational parameters, methodological consistencies, and quantitative algorithm performance metrics.
Approach.
Following preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines, studies were included if they reported algorithmic details of CPPopt, MAPopt, or BISopt derivation and provided at least six of seven core technical parameters (raw data sampling frequency, CVR index preprocessing, binning, data window size for optimality curve fitting, curve fitting method, update frequency, and yield), which were extracted during data extraction. Additional data captured included patient cohort characteristics, study objective, and CVR assessment technology.
Main results.
20 studies met the inclusion criteria: 13 described CPPopt, 6 described MAPopt, and 2 described BISopt derivation algorithms. CPPopt algorithms predominantly used the pressure reactivity index (PRx) as the CVR index, 5 mmHg binning, and second-order polynomial curve fitting, with frequent minute-by-minute updates and multi-window averaging. MAPopt algorithms primarily used near-infrared spectroscopy-derived indices such as hemoglobin volume index and cerebral oximetry index (COx), while BISopt studies combined electroencephalogram monitoring with PRx or COx. Algorithmic yield ranged from 45.6% to 100%, depending on the preprocessing strategy and curve-fitting quality. Based on the existing literature, we found CPPopt derivation remains the most mature and widely studied algorithm, while MAPopt and BISopt are emerging modalities with growing interest.
Significance.
Despite high feasibility across studies, significant methodological variability limits the comparability of the found algorithms. Standardized algorithm reporting is needed to support widespread clinical adoption of autoregulation-guided physiological targets.
The following article is
Open access
A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
D Sun
et al
2026
Physiol. Meas.
47
03NT01
View article
, A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
PDF
, A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
Objective.
A novel bionic esophageal device was developed to assess human swallowing function and orogastric transit, aiming ultimately to improve diagnostics for dysphagia. This miniaturized, tethered device records axial pressures, orientation, and acceleration during esophageal transit, thereby providing a dynamic view of the swallowing process.
Approach
In first-in-human feasibility tests, two healthy volunteers safely swallowed the device repeatedly in seated and supine positions.
Main Results
. The system produced transit and pressure profiles comparable to existing technologies, with prolonged transit times observed in the supine position, e.g. transit time in seated position was median 6 s (6–23) and in the supine posture median 233 s [142–317]).
Significance
. These findings support the potential of this bionic device for studying esophageal motility in physiological studies as well as pathological conditions in dysphagia patients, and for future translation to untethered capsule systems capable of full gastrointestinal transit analysis.
A domain adversarial network for small-sample obstructive sleep apnea detection using a single-lead piezoelectric sensor
Xuefeng Song
et al
2026
Physiol. Meas.
47
045009
View article
, A domain adversarial network for small-sample obstructive sleep apnea detection using a single-lead piezoelectric sensor
PDF
, A domain adversarial network for small-sample obstructive sleep apnea detection using a single-lead piezoelectric sensor
Objective.
In recent years, increasing attention to sleep health has accelerated development of wearable devices for home monitoring. Two critical challenges remain: providing an unobtrusive sensing solution suitable for long-term deployment, and designing algorithms that generalize across diverse users and acoustic environments when annotated data are limited.
Approach.
We develop a single-lead bone-conduction monitoring system and a distribution-aware detection model. The wearable system uses a piezoelectric sensor at the lateral throat to capture tracheal vibrations for continuous overnight monitoring, which improves user comfort relative to multi-lead polysomnography (PSG). For robust event recognition, we propose the convolutional memory adaptive prototypic network (CMAP), which combines convolutional–recurrent feature extraction with domain-adversarial training and a prototypical constraint to learn domain-invariant representations under limited labels. We evaluated our solution with a cohort of 95 participants undergoing simultaneous PSG and wearable monitoring.
Main results.
For snore and breath sound event detection during sleep, the model attained precision of 0.90 and recall of 0.93. The estimated Apnea–Hypopnea Index (AHI) from our system correlated strongly with PSG-derived AHI (Pearson
= 0.94,
< 0.001). The model achieved 83.2% accuracy in four-class severity classification across healthy, mild, moderate and severe obstructive sleep apnea (OSA).
Significance.
These results indicate that a single-sensor, distribution-aware approach can provide a reliable acoustic surrogate for AHI and clinically relevant severity classification. The proposed wearable and CMAP model offer a practical, comfortable, and cost-effective solution for early clinical triaging, scalable home-based OSA screening and longitudinal monitoring with low user burden.
PEAS: parametric EIT analysis software, a software to perform analyses on electrical impedance tomography data
Claas Händel 2026
Physiol. Meas.
47
045008
View article
, PEAS: parametric EIT analysis software, a software to perform analyses on electrical impedance tomography data
PDF
, PEAS: parametric EIT analysis software, a software to perform analyses on electrical impedance tomography data
Objective.
Electrical impedance tomography (EIT) is a powerful imaging technique for assessing regional ventilation, but its analysis remains challenging due to the diversity of input formats, acquisition protocols, and research objectives. This work aims to simplify and standardize EIT data analysis through the development of a modular, user-friendly software platform.
Approach.
We developed the parametric EIT analysis software (PEAS), a modular platform for EIT data analysis based on configurable, template-driven workflows. The software supports both raw voltage data with integrated image reconstruction and pre-reconstructed images, provides temporal detectors for breathing cycles and maneuvers, and reusable analysis components. These functionalities are accessed through a graphical user interface that enables interactive workflow configuration and execution.
Main results.
The implemented framework supports multiple vendor-specific data formats, including both raw voltage recordings and reconstructed image data. It provides automated detection of breathing cycles and respiratory maneuvers, as well as over 40 generic building blocks that can be combined into customized analysis pipelines. Typical workflows execute within seconds on standard hardware, enabling interactive use. A questionnaire-based user study indicated that the software is easy to learn and operate.
Significance.
By providing a standardized, extensible, and user-friendly environment for EIT data analysis, PEAS lowers the technical barrier to applying EIT in both research and clinical practice. This platform supports reproducibility, interoperability, and wider adoption of EIT for physiological monitoring and diagnostic applications. By offering a standardized yet extensible environment for EIT data analysis, PEAS reduces technical barriers in both research and clinical contexts. The platform promotes reproducibility, interoperability, and broader adoption of EIT for physiological monitoring and diagnostic applications.
The following article is
Open access
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Shagen Djanian
et al
2026
Physiol. Meas.
47
045007
View article
, Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
PDF
, Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.
This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.
Approach.
We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22–71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.
Main results.
The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.
Conclusion.
Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.
Significance.
This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
The following article is
Open access
Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
Parham Rezaei
et al
2026
Physiol. Meas.
47
045003
View article
, Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
PDF
, Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
Objective
. We investigated (i) if blood volume decompensation status (BVDS) can be trend-tracked by hemodynamic parameters, and (ii) if hemodynamic parameters capable of trend-tracking BVDS can be trend-tracked by the physio-markers derived from the physiological signals measured using wearable sensors.
Approach
. In 9 pigs undergoing controlled hemorrhage and blood transfusion, we measured gold standard arterial blood pressure (BP), heart rate (HR), stroke volume (SV), and cardiac output (CO) via invasive aortic BP and flow signals. In addition, we derived non-invasive physio-markers from the electrocardiogram, photoplethysmogram (PPG), and seismocardiogram signals measured using wearable sensors. Then, we determined the best hemodynamic parameters to trend-track BVDS by comparing their correlation with BVDS. Finally, we investigated the feasibility of trend-tracking BVDS via non-invasive physio-markers in terms of their correlation with hemodynamic parameters as well as BVDS.
Main results
. SV and CO could trend-track BVDS more consistently and explainably than BP and HR during hemorrhage and blood transfusion. The physio-markers of SV (the ratio between left ventricular ejection time (LVET) and pre-ejection period (PEP): LVET/PEP and PPG amplitude:
PPG
) and CO (HR·LVET/PEP and HR·
PPG
) showed close and monotonic relationships to SV (LVET/PEP: Spearman correlation 0.96 (0.93–0.98) and Pearson correlation 0.96 (0.93–0.98)) and CO (HR·LVET/PEP: Spearman correlation 0.95 (0.91–0.97) and Pearson correlation 0.91 (0.89–0.97)), and they likewise showed close and monotonic relationships to BVDS. However, substantial inter-individual variability in the hemodynamic parameters and their physio-markers was also observed.
Significance
. These findings suggest the feasibility of wearable-enabled hemodynamic monitoring during hemorrhage and blood transfusion, as well as the challenges therein.
Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions
Daniel Ray
et al
2026
Physiol. Meas.
47
045004
View article
, Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions
PDF
, Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions
Objective.
To improve fairness (reduced disparities across skin tones and sexes) and trust (well-calibrated uncertainty metrics that indicate unreliable predictions) in wrist-worn photoplethysmography (PPG) heart rate estimation. While PPG offers a convenient, low-cost method for continuous heart rate monitoring, accuracy is often degraded by motion artefacts and demographic variation.
Approach.
We collected a multi-wavelength (blue, green, red, IR) wrist-worn PPG dataset covering diverse activities, skin tones, and sexes, and developed a multi-branch PPG-accelerometer fusion convolutional neural network quantifying both aleatoric and epistemic uncertainty (the latter via Monte Carlo dropout, concrete dropout, and deep ensembles). We compared wavelength sets, quantified demographic effects, and applied uncertainty-aware post-processing to filter out high-uncertainty predictions. Evaluation used leave-one-subject-out cross validation across both the collected multi-wavelength dataset and five commonly used single-wavelength datasets.
Main results.
Multi-wavelength fusion improved accuracy under motion, with blue-green-red-IR achieving the lowest mean absolute error (MAE). Baseline error was higher for participants with darker skin tones and for females; uncertainty-guided rejection reduced MAE and effectively eliminated these gaps (reducing parity gaps from 1.2 BPM to 0.0 BPM for skin tone). Concrete dropout provided the best calibration-accuracy-efficiency trade-off, achieving the lowest average miscalibration area (MA
) while maintaining competitive MAE.
Significance.
Combining calibrated uncertainty with group-aware evaluation yields a more fair and trustworthy wrist-worn PPG heart rate estimation pipeline, supporting responsible deployment in consumer and sports contexts and informing bias-aware clinical development.
The following article is
Open access
The challenge in finding a simple, accurate, reliable, and affordable tool for the objective assessment of excessive daytime sleepiness (EDS)
Arie Oksenberg
et al
2026
Physiol. Meas.
47
01TR02
View article
, The challenge in finding a simple, accurate, reliable, and affordable tool for the objective assessment of excessive daytime sleepiness (EDS)
PDF
, The challenge in finding a simple, accurate, reliable, and affordable tool for the objective assessment of excessive daytime sleepiness (EDS)
Excessive daytime sleepiness (EDS) refers to a physiological state where individuals have difficulty remaining alert during the day. Managing EDS is particularly challenging to study and treat due to its multifaceted nature. Assessment methods include both subjective and objective approaches. Subjective evaluation often relies on simple, widely accepted, and widely used questionnaires; however, these tools are inherently limited by self-reporting bias. Objective assessment, on the other hand, primarily involves two well-known and reliable tests, but these are costly, time-consuming, and impractical for use outside of sleep units. Therefore, developing an objective tool that can quickly and accurately detect a decline in alertness, while remaining reliable, easy to use, and affordable, is of critical importance for sleep clinicians, safety organizations, and researchers. According to PRISMA guidelines, we did a systematic analysis of 95 studies that used photoplethysmography (PPG) for assessing EDS, drowsiness, and/or fatigue during the last 15 years (2010–2025). With advances in wearable technology, particularly through PPG and artificial intelligence, achieving this goal may be attainable. The next essential step is rigorous validation against established gold-standard tests to ensure the tool meets scientific and clinical standards for widespread adoption.
Electrical impedance tomography for stroke volume monitoring: a narrative review on signal processing, experimental and clinical applications
Yuqiao Peng
et al
2026
Physiol. Meas.
47
01TR01
View article
, Electrical impedance tomography for stroke volume monitoring: a narrative review on signal processing, experimental and clinical applications
PDF
, Electrical impedance tomography for stroke volume monitoring: a narrative review on signal processing, experimental and clinical applications
Objective.
As cardiovascular diseases continue to rise, the accurate and convenient calculation of stroke volume (SV) and cardiac output (CO) has become an important topic. Studies have shown that electrical impedance tomography (EIT) can provide continuous non-invasive SV measurements. Despite its potential, a review of the various calculation methods for EIT-based SV and CO, along with their clinical utility, is lacking.
Approach
. A literature search was conducted on PubMed and Web of Science Core Collection. Full-text research articles in English were reviewed and discussed.
Main results
. In recent years, advancements in technology, clinical research, and intelligent algorithms have revealed EIT’s substantial potential in SV monitoring.
Significance
. This article offers a review of the evolution of EIT technology in measuring SV, introducing various calculation methods, their advantages, challenges, and clinical applications.
The following article is
Open access
Bioimpedance for peripheral edema assessment in heart failure and clinical practice: a systematic review
Shania Tubana-Dean
et al
2025
Physiol. Meas.
46
11TR01
View article
, Bioimpedance for peripheral edema assessment in heart failure and clinical practice: a systematic review
PDF
, Bioimpedance for peripheral edema assessment in heart failure and clinical practice: a systematic review
Objective.
Peripheral edema is a common issue among elderly individuals with chronic conditions such as heart failure (HF). Continuous, non-invasive monitoring may enable earlier intervention, reduced hospital readmissions, and improved quality of life. This systematic review aims to evaluate the use of bioimpedance (BI) as a method for monitoring peripheral edema, with a particular focus on portable and wearable applications for remote health management.
Approach.
A systematic search was conducted across PubMed, IEEE Xplore, and Web of Science to identify studies utilizing BI for the detection or monitoring of lower limb edema with potential for portability or wearability.
Main results.
Fourteen studies met the inclusion criteria. Five studies focused on HF patients, while nine involved other populations, such as healthy individuals, patients with limb injuries, or those on hemodialysis. Ten studies featured or proposed portable BI devices, whereas four remained at the proof-of-concept stage without portable implementations. There was significant variability in device design, measurement protocols, and target populations. While existing results show promise, few studies evaluated systems in real-world or long-term monitoring scenarios.
Significance.
BI is a promising, non-invasive approach for the continuous monitoring of peripheral edema, particularly in remote and home-based settings. However, current research is limited by small sample sizes, lack of standardization, and minimal validation in diverse, real-world environments. Further development of wearable systems and robust clinical validation is essential to support broader clinical adoption.
A systematic review of contactless respiratory rate measurement using RGB cameras
Sreya Deb Srestha and Sungho Kim 2025
Physiol. Meas.
46
09TR01
View article
, A systematic review of contactless respiratory rate measurement using RGB cameras
PDF
, A systematic review of contactless respiratory rate measurement using RGB cameras
Objective
. The advancement of contactless methods of measuring the respiratory rate (RR) using RGB cameras demonstrates a significant potential for improving patient care in various environments. As these methods offer reliable and discreet monitoring, they can prevent severe health complications and improve outcomes for patients facing challenges accessing traditional healthcare facilities.
Approach
. This systematic review explores recent advancements in RR estimation using RGB cameras, focusing on assessing publicly available datasets and effective signal preprocessing methods. We also conducted a comprehensive analysis by comparing RGB camera-based approaches with other sensor modalities and discussed potential future research directions and indicated the necessity of developing new approaches that would mitigate existing challenges and would enhance the accuracy and reliability of non-contact RR measurement methods.
Main results
. We analyzed existing public datasets, assessing their diversity in lighting, skin tone, and motion, alongside the camera hardware configurations, including frame rate and resolution, utilizing different filter and feature-based techniques. While deep learning and hybrid models achieved lower errors under ideal indoor lighting and minimal motion, performance significantly declined in low light, high motion, or complex uncontrolled environments. In contrast, other sensor modalities, such as thermal and infrared sensors, achieved high accuracy across a wide range of conditions, but at greater hardware cost and system complexity, while RGB cameras remained the most cost-effective option, trading off precision for accessibility.
Significance
. RGB camera-based RR monitoring systems have the potential for robust applicability in clinical and nonclinical settings such as telemedicine platforms for monitoring patients breathing rates (BRs) in real time. This review highlights existing research gaps, such as insufficient real-world datasets and sensitivity to environmental variance, and emphasizes on the importance of acquiring datasets based on complex real-world scenarios, standardized benchmarks, multi-sensor fusion for addressing current limitations, and deep neural network architecture implementation for reliable non-contact RR estimation for real-world applications.
The following article is
Open access
Physics-informed neural networks for physiological signal processing and modeling: a narrative review
Anni Zhao
et al
2025
Physiol. Meas.
46
07TR02
View article
, Physics-informed neural networks for physiological signal processing and modeling: a narrative review
PDF
, Physics-informed neural networks for physiological signal processing and modeling: a narrative review
Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretability. Like many other fields in engineering and science, the analysis of physiological signals has been influenced by PINNs in recent years. This manuscript provides a comprehensive overview of PINNs from various perspectives in the physiological signal analysis domain. After exploring the literature and screening the search results, more than 40 key studies in the related domain are selected and categorized based on both practically and theoretically significant perspectives, including input data types, applications, physics-informed models, and neural network architectures. While the advantages of PINNs in tackling forward and inverse problems in physiological signal contexts are highlighted, challenges such as noisy inputs, computational complexity, loss function types, and overall model configuration are discussed, providing insights into future research directions and improvements. This work can serve as a guiding resource for researchers exploring PINNs in biomedical and physiological signal processing, paving the way for more precise, data-efficient, and clinically relevant solutions.
A multimodal fusion network for heart sound abnormality detection and classification
Nguyen et al
View accepted manuscript
, A multimodal fusion network for heart sound abnormality detection and classification
PDF
, A multimodal fusion network for heart sound abnormality detection and classification
Objective: Accurate physiological assessment of cardiac function from heart sounds remains challenging due to background noise, variable heart rates, and the need for reliable cardiac-cycle segmentation. This study aimed to develop a fully E2E deep learning framework that extracts diagnostic information directly from raw heart sound recordings for cardiac abnormality detection and classification. Approach: We propose HS-MMNet, an E2E multi-modal deep learning framework designed for physiological heart sound analysis. Recordings are preprocessed (normalization and 25–400 Hz bandpass filtering) and divided into fixed-length 2.5-s segments. A Convolution Head with multi-atrous spatial pyramid and channel-spatial attention extracts fine-grained local temporal patterns from the filtered 1-D waveform. A Transformer Head captures long-range spectro-temporal dependencies from Log-Mel spectrograms. These hypotheses are iteratively fused by a novel Multi-Hypothesis Cross-Attention (MH-CA) module with cyclic query-key-value assignment and a hypothesis-mixing MLP, enabling rich cross-site interaction and effective suppression of noise and non-informative regions. Recording-level classification is obtained via a fully connected layer. Main results: On the PhysioNet/CinC Challenge 2016 dataset, HS-MMNet achieved 94.80% accuracy, 92.10% sensitivity, 96.85% specificity, 87.50% precision, and 89.74% F1-score, outperforming all previously reported methods. On the balanced five-class Yaseen dataset (normal, aortic stenosis, mitral regurgitation, mitral stenosis, mitral valve prolapse), it attained 99.60% macro-averaged precision, recall, and F1-score with only four misclassifications in 1000 recordings, establishing new state-of-the-art (SOTA) benchmarks. Significance: HS-MMNet represents an advance in automated physiological measurement from heart sounds. By eliminating cardiac cycle detection and multi-channel requirements while achieving SOTA diagnostic performance, it provides a practical, scalable solution for accurate cardiovascular screening with primary-care and low-resource settings.
Simultaneous macrovasculature and microvasculature cerebral autoregulation derived from the transfer function analysis of integrated cerebral haemodynamic data in older adults
Ball et al
View accepted manuscript
, Simultaneous macrovasculature and microvasculature cerebral autoregulation derived from the transfer function analysis of integrated cerebral haemodynamic data in older adults
PDF
, Simultaneous macrovasculature and microvasculature cerebral autoregulation derived from the transfer function analysis of integrated cerebral haemodynamic data in older adults
Objective: Cerebral autoregulation (CA) maintains stable cerebral blood flow (CBF) despite mean arterial pressure (MAP) fluctuations. Transcranial Doppler ultrasonography (TCD) and Near-Infrared Spectroscopy (NIRS) report CBF surrogates and facilitate CA assessment. This study aimed to investigate the distinct information they provide about dynamic CA (dCA) responses to MAP step changes Approach: Simultaneous TCD-NIRS was performed on 28 healthy older participants alongside continuous measurements of beat-to-beat and breath-to-breath MAP, heart rate (HR) and end-tidal CO2. Transfer function analysis (TFA) was performed, varying input/output metrics including MAP, middle and posterior cerebral artery blood flow velocity (MCAv/PCAv) and oxyhaemoglobin (HbO2), comparing macro- and microvasculature dCA, respectively. Main Results: No differences in regional MAP step change HbO2 responses were found across eight pre-frontal (p=0.14) or four averaged regions (p=0.69). There was a significant effect of time in HbO2 responses to MAP step change (p<0.001), and to MCAv step change (p=0.016). There were also significant differences between HbO2 and MCAv and PCAv responses (p<0.001). Distinct TCD and NIRS step responses suggest much slower dCA responses in the microvasculature, compared to MCA and PCA, without regional differences. Significance: Further investigation into regional dCA differences is needed alongside potential benefits of simultaneous TCD-NIRS in pathological states.
Acute effects of FIFA 11+ warm-up on skin temperature in male and female amateur soccer players
Caudet et al
View accepted manuscript
, Acute effects of FIFA 11+ warm-up on skin temperature in male and female amateur soccer players
PDF
, Acute effects of FIFA 11+ warm-up on skin temperature in male and female amateur soccer players
Objective: Warm-up is a fundamental part of the training session and competition preparation, improving performance and reducing sports injuries. The FIFA 11+ is a specific evidence-based routine created to enhance neuromuscular performance and prevent lower-limb injuries. Infrared thermography (IRT) is a non-invasive tool for monitoring tissue state and thermoregulation responses. This study examined the acute effects of the FIFA 11+ warm-up on skin surface temperature (Tsk) patterns of the dominant lower limb in amateur football players using IRT. Approach: A pre-post observational design was applied to 120 amateur players (60 men, 60 women) before a match. Baseline and post-intervention Tsk measurements were acquired with a FLIR T540-EST camera following the ThermoINEF protocol. Main results: Significant post-warm-up Tsk reductions were detected in proximal muscle regions, particularly in quadriceps and adductors, with a reduction of -1.9 to -2.4ºC (ES = -1.63 to -1.92, large) in women and -0.7 to -1.2ºC (ES = -0.66 to -1.07, moderate) in men. Conversely, distal regions such as the anterior plantar arch showed marked Tsk increases of +2.6ºC (ES = 1.83, large) in women; +2.1ºC (ES = 1.42, large) in men. Men exhibited higher absolute Tsk values overall (η2 ≈ 0.17-0.26), whereas women displayed greater relative percentage changes, including sex-specific Achilles tendon response (a decrease in women versus a slight increase in men). Significance: FIFA 11+ induces heterogeneous, region- and sex- dependent thermal adaptations, supporting the use of IRT as a valid tool for individualized warm-up monitoring and optimization in football.
Investigating the interrelationship of pulse wave dynamics across the menstrual cycle and pregnancy
Yang et al
View accepted manuscript
, Investigating the interrelationship of pulse wave dynamics across the menstrual cycle and pregnancy
PDF
, Investigating the interrelationship of pulse wave dynamics across the menstrual cycle and pregnancy
Objective: This study aims to investigate the pulse wave characteristics across the menstrual cycle and pregnancy, comparing their cardiovascular adaptations to elucidate shared physiological mechanisms and potential predictive markers for pregnancy-related cardiovascular risks. Approach: The study analyzes pulse wave characteristics, including hemodynamic parameters (cardiac output and systemic vascular resistance), waveform features, and pulse wave characteristic indices. The analysis spans two physiological cycles: (1) the menstrual cycle, with parallel evaluations of normal and dysmenorrhea groups; and (2) stratified pregnancy stages (early, mid, and late), with comparative assessments between normal and abnormal blood parameter groups. Main Results: Both the menstrual cycle and pregnancy exhibit analogous pulse wave variations, reflecting hemodynamic adjustments in cardiac output and vascular elasticity. Pregnancy stages demonstrate progressive pulse wave alterations, with abnormal blood parameters correlating with distinct waveform deviations. Menstrual cycle patterns provide a foundational model for these adaptive changes. Significance: The findings reveal a physiological continuum between menstrual and gestational cardiovascular adaptations, highlighting pulse wave analysis as a potential tool for early risk stratification. This study advances the understanding of female cardiovascular dynamics, offering implications for targeted health monitoring and intervention strategies.
Evaluating pulse oximeters in ICU according to the new FDA guidances: skin tone and LED spectra assessment
Barros et al
View accepted manuscript
, Evaluating pulse oximeters in ICU according to the new FDA guidances: skin tone and LED spectra assessment
PDF
, Evaluating pulse oximeters in ICU according to the new FDA guidances: skin tone and LED spectra assessment
Objective. To evaluate whether pulse oximeters cleared under older U.S. Food and Drug Administration (FDA) standards remain accurate in ICU patients under newer draft guidance, to assess their performance against different regulatory metrics, and to investigate whether skin tone or LED spectral characteristics could be associated with measurement bias. Approach. In this prospective ICU study, three regulatory-cleared pulse oximeters were compared against arterial blood gas analysis. Skin pigmentation was characterized using the Individual Typology Angle (ITA°), and differential bias was estimated following FDA recommendations. Red and infrared LED emissions were measured using a fibre-optic spectrometer, and Gaussian models were used to extract peak wavelengths. Main Results. Two low-cost devices demonstrated large mean bias, wide limits of agreement (LoA), and poor correlation with arterial blood gas (ABG), failing both 2013 FDA guidance (accuracy root mean square, ARMS ≤ 3%) and updated FDA accuracy requirements. The clinical bedside monitor showed lower bias and minimal pigmentation-related differential error, though still above the proposed ARMS threshold. All devices remained inside the 2017 ISO Standard (ARMS ≤ 4%). Spectral analysis revealed that the most accurate device used a longer infrared peak and a slightly shorter red peak, a combination that could influence separation at key hemoglobin absorption wavelengths and potentially reduce melanin-related interference. Significance. These findings indicate that some oximeters cleared under older standards may underperform in ICU patients when assessed against newer accuracy proposals, underscoring the importance of reevaluating device performance as guidance evolves.
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Open access
Inpainting for artifact restoration in rPPG signals: a tool for real-world applicability in wellness and driver monitoring systems
Félix Nieto-del-Amor
et al
2026
Physiol. Meas.
View article
, Inpainting for artifact restoration in rPPG signals: a tool for real-world applicability in wellness and driver monitoring systems
PDF
, Inpainting for artifact restoration in rPPG signals: a tool for real-world applicability in wellness and driver monitoring systems
Objective. Photoplethysmography (PPG) and remote photoplethysmography (rPPG) are widely used non-invasive techniques for monitoring cardiovascular parameters. However, signal artifacts from motion, lighting variations, and environmental noise pose significant challenges for accurate physiological measurement, particularly in non-contact rPPG systems. To address these issues, we propose a novel generative inpainting framework designed to restore corrupted segments of PPG and rPPG signals. Approach. Our method leverages a large-scale synthetic dataset that spans a broad range of heart rates (30–180 beats per minute) and incorporates diverse artifact profiles to simulate real-world conditions. The inpainting model is built upon a custom Wasserstein GAN architecture, using a gradient penalty to ensure stable adversarial training. This selective reconstruction approach targets only the corrupted segments while preserving the integrity of high-quality signal portions. Main results. Results show that the proposed framework improves signal quality compared to corrupted signals. For synthetic datasets spanning heart rates from 30 to 180 beats per minute, signal-to-noise ratio increases from approximately −0.65–−0.20 dB to 3.27–4.16 dB after inpainting, while the mean absolute error decreases from 0.08–0.09 to 0.05–0.06. Feature-level similarity also improves, with Fréchet Encoder Distance reduced from 0.12 to 0.03 for real PPG and from 0.07 to 0.01 for real rPPG, and consistent reductions observed across all synthetic heart-rate ranges (from 0.23–0.47 to 0.01–0.04). Heart-rate estimates derived from the reconstructed signals are statistically equivalent to those obtained from clean references. Significance. The proposed generative inpainting framework effectively restores degraded PPG and rPPG signals and preserves heart-rate estimates, supporting its use in non-critical physiological monitoring applications such as wellness monitoring and automotive contexts. Validation on real data was limited to relatively clean, resting-state recordings; further studies are required to assess performance under high-motion and real-world conditions.
The following article is
Open access
Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Shagen Djanian
et al
2026
Physiol. Meas.
47
045007
View article
, Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
PDF
, Towards real-time sleep stage classification: a deep learning approach leveraging PPG and ECG
Objective.
This work aims to enable adaptive consumer sleep technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data.
Approach.
We propose an end-to-end deep learning approach leveraging Photoplethysmography (PPG) signals, commonly available in CSTs. Model performance is improved by pretraining with electrocardiography (ECG) from the large-scale multi-ethnic study of atherosclerosis dataset datasets. Training and evaluation are conducted with the Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology and an additional dataset comprising of 13 participants (aged 22–71 years) without prior known sleep disorders. The dataset contains combined synchronized polysomnography (PSG) and Empatica E4 wearable data, annotated with American Academy of Sleep Medicine (AASM) sleep stages.
Main results.
The proposed method demonstrates sleep stage classification from minimally processed PPG signals for real-time intervention. While ECG-trained models are not directly transferable to PPG, fine-tuning significantly improves performance, achieving up to a 29% increase in multi-stage classification accuracy.
Conclusion.
Pretraining with ECG and fine-tuning with PPG increases sleep stage classification for end-to-end deep learning models, exceeding previous efforts particularly in 3-stage sleep classification.
Significance.
This work contributes to sleep health by developing a sleep stage classification model for minimally processed PPG sensor data and takes a step further towards making adaptive CSTs feasible for use with wearable sensors.
The following article is
Open access
Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
Parham Rezaei
et al
2026
Physiol. Meas.
47
045003
View article
, Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
PDF
, Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: opportunities and challenges
Objective
. We investigated (i) if blood volume decompensation status (BVDS) can be trend-tracked by hemodynamic parameters, and (ii) if hemodynamic parameters capable of trend-tracking BVDS can be trend-tracked by the physio-markers derived from the physiological signals measured using wearable sensors.
Approach
. In 9 pigs undergoing controlled hemorrhage and blood transfusion, we measured gold standard arterial blood pressure (BP), heart rate (HR), stroke volume (SV), and cardiac output (CO) via invasive aortic BP and flow signals. In addition, we derived non-invasive physio-markers from the electrocardiogram, photoplethysmogram (PPG), and seismocardiogram signals measured using wearable sensors. Then, we determined the best hemodynamic parameters to trend-track BVDS by comparing their correlation with BVDS. Finally, we investigated the feasibility of trend-tracking BVDS via non-invasive physio-markers in terms of their correlation with hemodynamic parameters as well as BVDS.
Main results
. SV and CO could trend-track BVDS more consistently and explainably than BP and HR during hemorrhage and blood transfusion. The physio-markers of SV (the ratio between left ventricular ejection time (LVET) and pre-ejection period (PEP): LVET/PEP and PPG amplitude:
PPG
) and CO (HR·LVET/PEP and HR·
PPG
) showed close and monotonic relationships to SV (LVET/PEP: Spearman correlation 0.96 (0.93–0.98) and Pearson correlation 0.96 (0.93–0.98)) and CO (HR·LVET/PEP: Spearman correlation 0.95 (0.91–0.97) and Pearson correlation 0.91 (0.89–0.97)), and they likewise showed close and monotonic relationships to BVDS. However, substantial inter-individual variability in the hemodynamic parameters and their physio-markers was also observed.
Significance
. These findings suggest the feasibility of wearable-enabled hemodynamic monitoring during hemorrhage and blood transfusion, as well as the challenges therein.
The following article is
Open access
Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads
Yu-Jen Cheng
et al
2026
Physiol. Meas.
47
045006
View article
, Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads
PDF
, Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads
Objective.
Continuous blood pressure (BP) monitoring is crucial for detecting nocturnal hypertension and acute hemodynamic changes. Conventional cuff-based methods disrupt sleep and miss transient BP fluctuations from sleep-related events or instability. Cuffless methods, such as pulse transit time (PTT), offer potential but often struggle to reliably track acute BP fluctuations due to complex and nonlinear hemodynamics.
Approach.
We developed a capacitive sensing pad system incorporating PTT with additional hemodynamic features for unobtrusive, continuous BP monitoring in supine subjects. The pad contains ultra-sensitive single-electrode capacitive (SEC) sensors made from carbon nanotube composites. In passive contact, SEC sensor on the back of the chest captures intrathoracic blood volume changes through deep tissue permittivity and ballistocardiography, while another under the leg detects arterial pulse-induced tissue vibrations. PTT is derived from the temporal delay between chest and leg signals. A neural network model incorporates PTT and intrathoracic blood volume features to improve BP estimation.
Main results.
In human trials (
= 30) with arm-cuff reference, the system showed strong correlation (
⩾ 0.94), with a mean error (ME) ⩽ 0.1 mmHg and standard deviation (SD) ⩽ 5.6 mmHg. In a subset (
= 8) with continuous finger-cuff reference, it maintained strong correlation (
⩾ 0.89), with a ME ⩽ 0.2 mmHg and SD ⩽ 7.8 mmHg.
Significance.
These results suggest the possibility of bed-based unobtrusive and continuous BP monitoring in the supine position.
The following article is
Open access
Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
Rakibul Hasan
et al
2026
Physiol. Meas.
47
035014
View article
, Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
PDF
, Algorithmic derivation of optimal CPP, MAP, and BIS targets from cerebrovascular reactivity indices: a systematic scoping review
Objective.
Autoregulation-guided physiological targeting, using metrics such as optimal cerebral perfusion pressure (CPPopt), optimal mean arterial pressure (MAPopt), and optimal bispectral index (BISopt), has emerged as a promising strategy for improving patient outcomes in critical care and neuromonitoring. These targets, derived from the continuous assessment of cerebrovascular reactivity (CVR) indices, are increasingly being studied for their potential to individualize patient management. This review aimed to identify and characterize existing literature detailing the derivation algorithms of CPPopt, MAPopt, and BISopt, focusing on key computational parameters, methodological consistencies, and quantitative algorithm performance metrics.
Approach.
Following preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews guidelines, studies were included if they reported algorithmic details of CPPopt, MAPopt, or BISopt derivation and provided at least six of seven core technical parameters (raw data sampling frequency, CVR index preprocessing, binning, data window size for optimality curve fitting, curve fitting method, update frequency, and yield), which were extracted during data extraction. Additional data captured included patient cohort characteristics, study objective, and CVR assessment technology.
Main results.
20 studies met the inclusion criteria: 13 described CPPopt, 6 described MAPopt, and 2 described BISopt derivation algorithms. CPPopt algorithms predominantly used the pressure reactivity index (PRx) as the CVR index, 5 mmHg binning, and second-order polynomial curve fitting, with frequent minute-by-minute updates and multi-window averaging. MAPopt algorithms primarily used near-infrared spectroscopy-derived indices such as hemoglobin volume index and cerebral oximetry index (COx), while BISopt studies combined electroencephalogram monitoring with PRx or COx. Algorithmic yield ranged from 45.6% to 100%, depending on the preprocessing strategy and curve-fitting quality. Based on the existing literature, we found CPPopt derivation remains the most mature and widely studied algorithm, while MAPopt and BISopt are emerging modalities with growing interest.
Significance.
Despite high feasibility across studies, significant methodological variability limits the comparability of the found algorithms. Standardized algorithm reporting is needed to support widespread clinical adoption of autoregulation-guided physiological targets.
The following article is
Open access
Thoracic electrical bioimpedance in a Lissajous plane: pre–post smoking changes and PCA of ellipse metrics
Balleza José-Marco
et al
2026
Physiol. Meas.
47
035013
View article
, Thoracic electrical bioimpedance in a Lissajous plane: pre–post smoking changes and PCA of ellipse metrics
PDF
, Thoracic electrical bioimpedance in a Lissajous plane: pre–post smoking changes and PCA of ellipse metrics
Objective.
Thoracic electrical bioimpedance (TEB) provides non-invasive, radiation-free monitoring of breathing. The objective of this study was to evaluate a magnitude–phase representation of TEB as a geometric and descriptive framework for respiratory signals, using short-term smoking as a test perturbation rather than a primary physiological endpoint.
Approach.
Twenty-eight adult smokers (17 women, 11 men) were measured immediately before and after smoking. TEB was acquired at 50 kHz using a four-electrode thoracic configuration, and tidal volume was recorded with a pneumotachometer. Changes in impedance magnitude (|Δ
|) and phase (Δ
) were processed using mean-centering, Hanning windowing, Fourier transformation, Gaussian band filtering around the respiratory peak, and inverse reconstruction. Lissajous plots were constructed from Δ|
|–Δ
signals, and geometric descriptors including semi-axes (
δx, δy
), inclination angle (
), ellipse area (
), eccentricity (
), and baseline offsets were extracted. Paired statistical tests were applied according to data distribution, and principal component analysis (PCA) was used to organize multiple descriptors and reduce redundancy.
Main results.
Univariate analyses showed no significant pre–post differences for most variables, except for a higher mean |Δ
| amplitude in men. In PCA space, ellipse area (
) showed consistent differences between pre- and post-smoking distributions across sexes. These differences reflected changes in joint magnitude–phase dispersion rather than statistically significant physiological effects. Inclination, semi-axes, and eccentricity showed substantial overlap between conditions. PCA provided low-dimensional representations that facilitated visualization and comparison of magnitude–phase patterns.
Significance.
Representing TEB signals as magnitude–phase Lissajous ellipses provides an intuitive and repeatable geometric representation of breathing. Ellipse area is proposed as a composite geometric descriptor of joint magnitude–phase variability, intended for representation and comparison rather than direct physiological inference. This non-invasive and computationally simple framework uses standard hardware and may support future methodological developments in respiratory signal analysis.
The following article is
Open access
A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
D Sun
et al
2026
Physiol. Meas.
47
03NT01
View article
, A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
PDF
, A novel approach to studying human orogastric transit with an ingestible bionic device. An early feasibility study
Objective.
A novel bionic esophageal device was developed to assess human swallowing function and orogastric transit, aiming ultimately to improve diagnostics for dysphagia. This miniaturized, tethered device records axial pressures, orientation, and acceleration during esophageal transit, thereby providing a dynamic view of the swallowing process.
Approach
In first-in-human feasibility tests, two healthy volunteers safely swallowed the device repeatedly in seated and supine positions.
Main Results
. The system produced transit and pressure profiles comparable to existing technologies, with prolonged transit times observed in the supine position, e.g. transit time in seated position was median 6 s (6–23) and in the supine posture median 233 s [142–317]).
Significance
. These findings support the potential of this bionic device for studying esophageal motility in physiological studies as well as pathological conditions in dysphagia patients, and for future translation to untethered capsule systems capable of full gastrointestinal transit analysis.
The following article is
Open access
Time-of-flight abdominal wall displacement for non-invasive longitudinal monitoring of pulmonary function
Wesam Bachir 2026
Physiol. Meas.
47
035009
View article
, Time-of-flight abdominal wall displacement for non-invasive longitudinal monitoring of pulmonary function
PDF
, Time-of-flight abdominal wall displacement for non-invasive longitudinal monitoring of pulmonary function
Objective
. Spirometry is the clinical gold standard for pulmonary function testing, but its reliance on mouthpiece-based airflow, trained supervision, and patient effort limits its use for frequent or home-based monitoring. This study investigates a single-point time-of-flight (TOF) sensor to capture abdominal wall displacement as a non-contact surrogate for spirometric indices.
Approach
. Displacement signals were recorded from 31 adult volunteers during quiet breathing, vital capacity (VC), and forced VC (FVC) manoeuvres, with simultaneous spirometry as reference. A preprocessing framework with filtering, segmentation, and feature extraction was developed, and subject-specific two-point calibration mapped displacement to lung volume. TOF-derived measures were compared to spirometry using agreement analyses, with BA plots used to quantify bias and limits of agreement for key indices.
Main results
. TOF signals accurately reproduced volume-related parameters: tidal volume, VC, and maximal voluntary ventilation agreed well with spirometry after calibration, with mean differences within clinically acceptable ranges. Estimation of the FEV₁/FVC ratio showed greater variability. After exclusion of one artifactual TOF measurement, BA analysis showed a small positive bias (∼+0.05) with limits of agreement of approximately −0.1 to +0.2. All TOF-derived ratios exceeded the clinical threshold of 0.7, supporting correct classification of normal ventilatory function in this cohort.
Significance
. These results indicate that although single-point TOF sensing cannot replace spirometry, it offers a non-contact, subject-specific calibration-minimal method for estimating pulmonary function, with promising applications in longitudinal monitoring, telehealth, and early screening.
The following article is
Open access
Analysis of federated learning on non-independent and identically distributed sleep data
Adriana Anido-Alonso and Diego Alvarez-Estevez 2026
Physiol. Meas.
47
035006
View article
, Analysis of federated learning on non-independent and identically distributed sleep data
PDF
, Analysis of federated learning on non-independent and identically distributed sleep data
Objective.
We investigate the application of federated learning (FL) across heterogeneous, non-independent and identically distributed (non-IID) sleep data. We evaluate three algorithms-federated stochastic gradient descent, federated averaging, and federated proximal (FedProx)-in a realistic setting where non-IID characteristics arise from distinct sensor configurations, varying acquisition protocols, and diverse patient populations across independent sleep cohort datasets.
Approach.
We employ a dual-layered evaluation framework. First, we systematically analyze the impact of local training epochs (
) and aggregation schemes (
weighted
and
unweighted
) on model convergence. Second, we introduce and adapt a generalized sub-sampling strategy designed to mitigate model drift caused by heterogeneous data distribution and volume imbalances across participating clients. To ensure robust external generalization, our evaluation utilizes six independent databases in a leave-one-database-out cross-validation scheme.
Main results.
Our analysis has evidenced that increasing the number of local training epochs adversely affects performance across all evaluated federated schemes. This confirms that extended local training exacerbates client drift, hindering global convergence. Furthermore,
weighted
aggregation consistently under-performs
unweighted
approaches, suggesting that disproportionate client contributions bias the global data representation. While the inclusion of a proximal term partially mitigates this instability by constraining local updates, the proposed
sub-sampling
strategy proves most effective. This approach yields consistent generalization results across all algorithms and minimizes performance downgrading, while significantly reducing computational overhead.
Significance.
This work addresses critical privacy concerns in centralized automated sleep staging by validating FL in realistic, multi-center scenarios. We provide evidence that decentralized strategies can achieve performance comparable to centralized methods, effectively overcoming data silos. Ultimately, this approach enables robust collaborative training while strictly maintaining data privacy-a fundamental requirement for widespread clinical implementation.
The following article is
Open access
Ophthalmology foundation models for clinically significant age macular degeneration detection
Benjamin A Cohen
et al
2026
Physiol. Meas.
47
035004
View article
, Ophthalmology foundation models for clinically significant age macular degeneration detection
PDF
, Ophthalmology foundation models for clinically significant age macular degeneration detection
Objective
. Self-supervised learning (SSL) has enabled vision transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhancing their generalization across domains. In retinal imaging, foundation models pretrained on either natural or ophthalmic data have shown promise, but the benefits of in-domain pretraining remain uncertain.
Approach
. To investigate this, we benchmark six SSL-pretrained ViTs on seven digital fundus image (DFI) datasets totaling 70 000 expert-annotated images for the task of moderate-to-late age-related macular degeneration (AMD) identification.
Main results
. Our results show that DINOv2, pretrained on natural images, shows similar performance than domain-specific models. These findings highlight the value of foundation models in improving AMD identification, and challenge the assumption that in-domain pretraining is necessary.
Significance
. We present our model AMDNet, which performs state-of-the-art out-of-domain AUROCs on six public datasets. Furthermore, we release BRAMD, an open-access dataset (
= 587) of DFIs with AMD labels from Brazil. Project page:
www.aimlab-technion.com/lirot-ai
More Open Access articles
Photoplethysmography and its application in clinical physiological measurement
John Allen 2007
Physiol. Meas.
28
R1
View article
, Photoplethysmography and its application in clinical physiological measurement
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, Photoplethysmography and its application in clinical physiological measurement
Photoplethysmography (PPG) is a simple and low-cost optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is often used non-invasively to make measurements at the skin surface. The PPG waveform comprises a pulsatile (‘AC’) physiological waveform attributed to cardiac synchronous changes in the blood volume with each heart beat, and is superimposed on a slowly varying (‘DC’) baseline with various lower frequency components attributed to respiration, sympathetic nervous system activity and thermoregulation. Although the origins of the components of the PPG signal are not fully understood, it is generally accepted that they can provide valuable information about the cardiovascular system. There has been a resurgence of interest in the technique in recent years, driven by the demand for low cost, simple and portable technology for the primary care and community based clinical settings, the wide availability of low cost and small semiconductor components, and the advancement of computer-based pulse wave analysis techniques. The PPG technology has been used in a wide range of commercially available medical devices for measuring oxygen saturation, blood pressure and cardiac output, assessing autonomic function and also detecting peripheral vascular disease. The introductory sections of the topical review describe the basic principle of operation and interaction of light with tissue, early and recent history of PPG, instrumentation, measurement protocol, and pulse wave analysis. The review then focuses on the applications of PPG in clinical physiological measurements, including clinical physiological monitoring, vascular assessment and autonomic function.
Infrared thermal imaging in medicine
E F J Ring and K Ammer 2012
Physiol. Meas.
33
R33
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, Infrared thermal imaging in medicine
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, Infrared thermal imaging in medicine
This review describes the features of modern infrared imaging technology and the standardization protocols for thermal imaging in medicine. The technique essentially uses naturally emitted infrared radiation from the skin surface. Recent studies have investigated the influence of equipment and the methods of image recording. The credibility and acceptance of thermal imaging in medicine is subject to critical use of the technology and proper understanding of thermal physiology. Finally, we review established and evolving medical applications for thermal imaging, including inflammatory diseases, complex regional pain syndrome and Raynaud's phenomenon. Recent interest in the potential applications for fever screening is described, and some other areas of medicine where some research papers have included thermal imaging as an assessment modality. In certain applications thermal imaging is shown to provide objective measurement of temperature changes that are clinically significant.
Wavelet-based motion artifact removal for functional near-infrared spectroscopy
Behnam Molavi and Guy A Dumont 2012
Physiol. Meas.
33
259
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, Wavelet-based motion artifact removal for functional near-infrared spectroscopy
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, Wavelet-based motion artifact removal for functional near-infrared spectroscopy
Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal.
How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation
F Scholkmann
et al
2010
Physiol. Meas.
31
649
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, How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation
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, How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation
Near-infrared imaging (NIRI) is a neuroimaging technique which enables us to non-invasively measure hemodynamic changes in the human brain. Since the technique is very sensitive, the movement of a subject can cause movement artifacts (MAs), which affect the signal quality and results to a high degree. No general method is yet available to reduce these MAs effectively. The aim was to develop a new MA reduction method. A method based on moving standard deviation and spline interpolation was developed. It enables the semi-automatic detection and reduction of MAs in the data. It was validated using simulated and real NIRI signals. The results show that a significant reduction of MAs and an increase in signal quality are achieved. The effectiveness and usability of the method is demonstrated by the improved detection of evoked hemodynamic responses. The present method can not only be used in the postprocessing of NIRI signals but also for other kinds of data containing artifacts, for example ECG or EEG signals.
Improved motion robustness of remote-PPG by using the blood volume pulse signature
G de Haan and A van Leest 2014
Physiol. Meas.
35
1913
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, Improved motion robustness of remote-PPG by using the blood volume pulse signature
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, Improved motion robustness of remote-PPG by using the blood volume pulse signature
Remote photoplethysmography (rPPG) enables contact-free monitoring of the blood volume pulse using a color camera. Essentially, it detects the minute optical absorption changes caused by blood volume variations in the skin. In this paper, we show that the different absorption spectra of arterial blood and bloodless skin cause the variations to occur along a very specific vector in a normalized RGB-space. The exact vector can be determined for a given light spectrum and for given transfer characteristics of the optical filters in the camera. We show that this ‘signature’ can be used to design an rPPG algorithm with a much better motion robustness than the recent methods based on blind source separation, and even better than the chrominance-based methods we published earlier. Using six videos recorded in a gym, with four subjects exercising on a range of fitness devices, we confirm the superior motion robustness of our newly proposed rPPG methods. A simple peak detector in the frequency domain returns the correct pulse-rate for 68% of total measurements compared to 60% for the best previous method, while the SNR of the pulse-signal improves from  − 5 dB to  − 4 dB. For a large population of 117
stationary
subjects we prove that the accuracy is comparable to the best previous method, although the SNR of the pulse-signal drops from  + 8.4 dB to  + 7.6 dB. We expect the improved motion robustness to significantly widen the application scope of the rPPG-technique.
An open access database for the evaluation of heart sound algorithms
Chengyu Liu
et al
2016
Physiol. Meas.
37
2181
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, An open access database for the evaluation of heart sound algorithms
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, An open access database for the evaluation of heart sound algorithms
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.
The following article is
Open access
The 2023 wearable photoplethysmography roadmap
Peter H Charlton
et al
2023
Physiol. Meas.
44
111001
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, The 2023 wearable photoplethysmography roadmap
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, The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
A method to standardize a reference of scalp EEG recordings to a point at infinity
Dezhong Yao 2001
Physiol. Meas.
22
693
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, A method to standardize a reference of scalp EEG recordings to a point at infinity
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, A method to standardize a reference of scalp EEG recordings to a point at infinity
The effect of an active reference in EEG recording is one of the oldest technical problems in EEG practice. In this paper, a method is proposed to approximately standardize the reference of scalp EEG recordings to a point at infinity. This method is based on the fact that the use of scalp potentials to determine the neural electrical activities or their equivalent sources does not depend on the reference, so we may approximately reconstruct the equivalent sources from scalp EEG recordings with a scalp point or average reference. Then the potentials referenced at infinity are approximately reconstructed from the equivalent sources. As a point at infinity is far from all the possible neural sources, this method may be considered as a reference electrode standardization technique (REST). The simulation studies performed with assumed neural sources included effects of electrode number, volume conductor model and noise on the performance of REST, and the significance of REST in EEG temporal analysis. The results showed that REST is potentially very effective for the most important superficial cortical region and the standardization could be especially important in recovering the temporal information of EEG recordings.
GREIT: a unified approach to 2D linear EIT reconstruction of lung images
Andy Adler
et al
2009
Physiol. Meas.
30
S35
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, GREIT: a unified approach to 2D linear EIT reconstruction of lung images
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, GREIT: a unified approach to 2D linear EIT reconstruction of lung images
Electrical impedance tomography (EIT) is an attractive method for clinically monitoring patients during mechanical ventilation, because it can provide a non-invasive continuous image of pulmonary impedance which indicates the distribution of ventilation. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT images because the reconstructed images are not well characterized. To address this issue, we develop a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This paper describes the unified approach to linear image reconstruction developed for GREIT. The framework for the linear reconstruction algorithm consists of (1) detailed finite element models of a representative adult and neonatal thorax, (2) consensus on the performance figures of merit for EIT image reconstruction and (3) a systematic approach to optimize a linear reconstruction matrix to desired performance measures. Consensus figures of merit, in order of importance, are (a) uniform amplitude response, (b) small and uniform position error, (c) small ringing artefacts, (d) uniform resolution, (e) limited shape deformation and (f) high resolution. Such figures of merit must be attained while maintaining small noise amplification and small sensitivity to electrode and boundary movement. This approach represents the consensus of a large and representative group of experts in EIT algorithm design and clinical applications for pulmonary monitoring. All software and data to implement and test the algorithm have been made available under an open source license which allows free research and commercial use.
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Erick A Perez Alday
et al
2020
Physiol. Meas.
41
124003
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, Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
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, Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Objective
: Vast 12-lead ECGs repositories provide opportunities to develop new machine learning approaches for creating accurate and automatic diagnostic systems for cardiac abnormalities. However, most 12-lead ECG classification studies are trained, tested, or developed in single, small, or relatively homogeneous datasets. In addition, most algorithms focus on identifying small numbers of cardiac arrhythmias that do not represent the complexity and difficulty of ECG interpretation. This work addresses these issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020.
Approach
: A total of 66 361 12-lead ECG recordings were sourced from six hospital systems from four countries across three continents; 43 101 recordings were posted publicly with a focus on 27 diagnoses. For the first time in a public competition, we required teams to publish open-source code for both training and testing their algorithms, ensuring full scientific reproducibility.
Main results
: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry. As with previous Challenges, high-performing algorithms exhibited significant drops (
10%) in performance on the hidden test data.
Significance
: Data from diverse institutions allowed us to assess algorithmic generalizability. A novel evaluation metric considered different misclassification errors for different cardiac abnormalities, capturing the outcomes and risks of different diagnoses. Requiring both trained models and code for training models improved the generalizability of submissions, setting a new bar in reproducibility for public data science competitions.
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1993-present
Physiological Measurement
doi: 10.1088/issn.0967-3334
Online ISSN: 1361-6579
Print ISSN: 0967-3334
Journal history
1993-present
Physiological Measurement
1980-1992
Clinical Physics and Physiological Measurement