Physiological measurement最新文献

{"title":"Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation.","authors":"Mads C F Hostrup, Anne Sofie Nielsen, Freja E Sørensen, Jesper O Kragballe, Morten U Østergaard, Emil Korsgaard, Samuel E Schmidt, Dan S Karbing","doi":"10.1088/1361-6579/adbe23","DOIUrl":"10.1088/1361-6579/adbe23","url":null,"abstract":"<p><p><i>Objective.</i>Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range.<i>Approach.</i>Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 s. A flow meter was used as reference. Furthermore, the PCA-autocorrelation method was compared with a similar single axis method.<i>Main results.</i>The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [-1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCA-autocorrelation method were within ±2 bpm of the reference. A Pearson correlation indicated a very strong correlation with<i>r</i> = 0.99 (p<0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [-14.9; 22.3 bpm], and<i>r</i> = 0.44 (p<0.001).<i>Significance.</i>The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution.","authors":"Peter H Charlton, Erick Javier Argüello-Prada, Jonathan Mant, Panicos A Kyriacou","doi":"10.1088/1361-6579/adb89e","DOIUrl":"10.1088/1361-6579/adb89e","url":null,"abstract":"<p><p><i>Objective:</i>photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The multi-scale peak & trough detection (<i>MSPTD</i>) algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the<i>MSPTD</i>algorithm for PPG beat detection, named<i>MSPTDfast (v.2)</i>.<i>Approach.</i>five potential improvements to<i>MSPTD</i>were identified and evaluated on four datasets.<i>MSPTDfast (v.2)</i>was designed by incorporating each improvement which on its own reduced execution time whilst maintaining a high<i>F</i>₁-score. After internal validation,<i>MSPTDfast (v.2)</i>was benchmarked against state-of-the-art beat detection algorithms on four additional datasets.<i>Main results.</i><i>MSPTDfast (v.2)</i>incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates >30 bpm. During internal validation<i>MSPTDfast (v.2)</i>was found to have an execution time of between approximately one-third and one-twentieth of<i>MSPTD</i>, and a comparable<i>F</i>₁-score. During benchmarking<i>MSPTDfast (v.2)</i>was found to have the highest<i>F</i>₁-score alongside<i>MSPTD</i>, and amongst one of the lowest execution times with only<i>MSPTDfast (v.1)</i>,<i>qppgfast</i>and<i>MMPD (v.2)</i>achieving shorter execution times.<i>Significance.</i><i>MSPTDfast (v.2)</i>is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the precision of impedance measurement from 5 kHz to 1 MHz through self-identification of parasitic parameters.","authors":"Yi She, Zeyi Jiang, Qin Liu, Sirui Qiao, Yixin Ma","doi":"10.1088/1361-6579/adb9b4","DOIUrl":"10.1088/1361-6579/adb9b4","url":null,"abstract":"<p><p><i>Objective.</i>Electrical impedance tomography (EIT) generates cross-sectional images through non-invasive impedance measurements from surface electrodes. While impedance above 200 kHz can reveal intracellular properties, most existing EIT images are published at frequencies below 200 kHz. When frequencies exceed 200 kHz, the accuracy of impedance measurements declines due to the distributed circuit parameters such as parasitic capacitance, on-resistance of switch and the series inductance, with a more significant impact on larger impedance. To overcome this limitation, this paper proposes an approach to enhance the precision of impedance measurement through self-identification of distributed parameter.<i>Approach.</i>Firstly, the distributed circuit parameters are identified via correction measurements of precision resistances in the frequency range from 5 kHz to 1 MHz; then, the circuit is accurately modeled; finally, transfer impedance measurements during imaging process are corrected using the established circuit model.<i>Main results.</i>The distributed circuit parameter self-identification method was verified through a goodness-of-fit test, confirming the consistency between the model's predicted values and the actual values of the component. The test results indicate that at 1 MHz, the relative residuals follow a right-skewed distribution with an average value of 0.08%, which demonstrates high model accuracy. At 1 MHz, the relative error after correction for the 499 Ω precision resistor measurement is reduced by 12.01%, and for the 56 pF precision capacitor in parallel with 249 Ω, the relative error after correction is 0.46%.<i>Significance.</i>The proposed method can extend the frequency range of EIT and other impedance technologies from below 200 kHz to up to 1 MHz, while ensuring good measurement accuracy.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bed sensor ballistocardiogram for non-invasive detection of atrial fibrillation: a comprehensive clinical study.","authors":"Jonas Sandelin, O Lahdenoja, I Elnaggar, R Rekola, A Anzanpour, S Seifizarei, M Kaisti, T Koivisto, J Lehto, J Nuotio, J Jaakkola, A Relander, T Vasankari, J Airaksinen, T Kiviniemi","doi":"10.1088/1361-6579/adbb52","DOIUrl":"10.1088/1361-6579/adbb52","url":null,"abstract":"<p><p><i>Objective.</i>Atrial fibrillation (AFib) is a common cardiac arrhythmia associated with high morbidity and mortality, making early detection and continuous monitoring essential to prevent complications like stroke. This study explores the potential of using a ballistocardiogram (BCG) based bed sensor for the detection of AFib.<i>Approach.</i>We conducted a comprehensive clinical study with night hospital recordings from 116 patients, divided into 72 training and 44 test subjects. The study employs established methods such as autocorrelation to identify AFib from BCG signals. Spot and continuous Holter ECG were used as reference methods for AFib detection against which BCG rhythm classifications were compared.<i>Results.</i>Our findings demonstrate the potential of BCG-based AFib detection, achieving 94% accuracy on the training set using a rule-based method. Furthermore, the machine learning model trained with the training set achieved an AUROC score of 97% on the test set.<i>Significance.</i>This innovative approach shows promise for accurate, non-invasive, and continuous monitoring of AFib, contributing to improved patient care and outcomes, particularly in the context of home-based or hospital settings.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.","authors":"Muqing Deng, Junsheng Guo, Boyan Li, Jingfen Yang, Xiaobo Zhang, Dandan Liang, Yanjiao Wang, Xiaoyu Huang","doi":"10.1088/1361-6579/adbab0","DOIUrl":"10.1088/1361-6579/adbab0","url":null,"abstract":"<p><p><i>Objective</i>. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although fruitful achievements have been reported on risk stratification of hypertension, the potential use of ambulatory blood pressure monitoring data is not well investigated. Different from single measuring blood pressure data, long-term blood pressure monitoring data can provide more comprehensive dynamical blood pressure information. Therefore, this paper proposes an intelligent hypertension risk stratification method based on ambulatory blood pressure monitoring data and improved machine learning algorithms.<i>Approach</i>. A total of 262 patients with hypertension are enrolled at People's Hospital of Yangjiang, in which 93 subjects are with simple hypertension and 169 subjects have hypertension with complication. Time-domain features, frequency-domain features, nonlinear dynamics features and correlation features underlying time-varying ambulatory blood pressure monitoring data are extracted to obtain discriminative feature representations. Synthetic minority over-sampling algorithm is applied to solve the problem of data balancing. The particle swarm optimization combined with kernel extreme learning machine is employed for feature fusion and optimization.<i>Main results</i>. The proposed method can yield a diagnostic accuracy of 93.7%, 97.8%, and 98.4% under two-, five- and ten-fold cross-validation, which demonstrates hypertension risk stratification in an intuitive, quantizable manner using multi-dimensional feature representation and learning.<i>Significance</i>. The proposed method is expected to provide early warning for latent serious cardiovascular diseases before obvious symptoms are present.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of occult hemorrhage using multivariate non-invasive technologies: a porcine study.","authors":"Navid Rashedi, Ethan K Murphy, Samuel B Klein, Alexandra Hamlin, Justin E Anderson, Joseph M Minichiello, Alexander L Lindqwister, Karen L Moodie, Zachary J Wanken, Jackson T Read, Victor A Borza, Jonathan T Elliott, Ryan J Halter, Vikrant S Vaze, Norman A Paradis","doi":"10.1088/1361-6579/adb4b8","DOIUrl":"10.1088/1361-6579/adb4b8","url":null,"abstract":"<p><p><i>Objective.</i>Occult hemorrhage (OH) can emerge subtly post-trauma, especially when internal bleeding is not yet severe enough to result in noticeable hemodynamic changes or shock. Despite normal appearances of traditional vital signs like heart rate (HR) and blood pressure (BP), clinically significant OH may be present, posing a critical diagnostic challenge. Early detection of OH, before vital signs begin to deteriorate, is vital as delays in identifying such conditions are linked to poorer patient outcomes. We analyze the performance of poly-anatomic multivariate technologies-including electrical impedance tomography (EIT), near-infrared spectroscopy (NIRS), electrical impedance spectroscopy (EIS), plethysmography (Pleth), and ECG-in a porcine model of OH. The goal was to detect OH without the need to know the subject's pre-established normal baseline.<i>Approach.</i>Forty female swine were bled at slow rates to create an extended period of subclinical hemorrhage, during which the animals' HR and BP remained stable before hemodynamic deterioration. Continuous vital signs, Pleth, and continuous non-invasive data were recorded and analyzed with the objective of developing an improved means of detecting OH. This detection was set up as a supervised voting classification problem where the measurement of each technology (minimally transformed) was used to train a classifier. A soft majority voting classification technique was then used to detect the existence of OH.<i>Main Results.</i>When comparing the prediction performance of the most significant univariate technology (EIT) to that of a poly-anatomic multivariate approach, the latter achieved higher area-under-the-curve (AUC) values from receiver operating characteristic analyses in almost every observation interval duration. In particular, after 21 min of continuous observation, the best AUC of the multivariate approach was 0.98, while that of the univariate approach was 0.92. The best multivariate technologies, in descending order, appeared to be EIT on the thorax, NIRS on the abdomen, and EIS on the thorax.<i>Significance.</i>In this clinically relevant porcine model of clinically OH, multivariate non-invasive measurements may be superior to univariate ones in detecting OH. Advanced technologies such as EIT, NIRS, and EIS exhibit considerably greater potential to accurately predict OH than standard physiological measurements. From a practical standpoint, our approach would not require the medical device to have prior access to non-hemorrhage baseline data for each patient. Early detection of OH using these technologies could improve patient outcomes by allowing for timely intervention before vital signs begin to deteriorate.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143399036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LumEDA: image luminance based contactless correlates of electrodermal responses.","authors":"Mayur Bhamborae, Elena N Schneider, Philipp Flotho, Alexander L Francis, Daniel J Strauss","doi":"10.1088/1361-6579/adb369","DOIUrl":"10.1088/1361-6579/adb369","url":null,"abstract":"<p><p><i>Objective</i>. Electrodermal activity (EDA) is a marker of psychophysiological arousal and is usually a measure of the skin conductance which is associated with sweat gland activity. Recent studies have shown that it is possible to estimate the EDA using contactless video based methods.<i>Approach</i>. Sensor EDA signals (SenEDA) and videos of the the palm were recorded simultaneously from over 30 participants under various stimuli (audio, video, cognitive and physiological). The luminance information from the video data was used to track sweat gland activity on the skin surface and extract the contactless signal luminance based EDA (LumEDA).<i>Main results</i>. Comparison of the SenEDA and LumEDA signals showed a high positive correlation between the two as expected.<i>Significance</i>. Under suitable illumination, simple spatial filters can be used to track sweat gland activity which can then be used to estimate signals analogous to the EDA. Such video based methods also facilitate spatio-temporal analysis of EDA correlates over larger areas of the body.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative adversarial networks with fully connected layers to denoise PPG signals.","authors":"Itzel A Avila Castro, Helder P Oliveira, Ricardo Correia, Barrie Hayes-Gill, Stephen P Morgan, Serhiy Korposh, David Gomez, Tania Pereira","doi":"10.1088/1361-6579/ada9c1","DOIUrl":"10.1088/1361-6579/ada9c1","url":null,"abstract":"<p><p><i>Objective.</i>The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.<i>Approach.</i>A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets.<i>Main results.</i>The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm.<i>Significance.</i>The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143009920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REDT: a specialized transformer model for the respiratory phase and adventitious sound detection.","authors":"Jianhong Wang, Gaoyang Dong, Yufei Shen, Xiaoling Xu, Minghui Zhang, Ping Sun","doi":"10.1088/1361-6579/adaf08","DOIUrl":"10.1088/1361-6579/adaf08","url":null,"abstract":"<p><p><i>Background and objective.</i>In contrast to respiratory sound classification, respiratory phase and adventitious sound event detection provides more detailed and accurate respiratory information, which is clinically important for respiratory disorders. However, current respiratory sound event detection models mainly use convolutional neural networks to generate frame-level predictions. A significant drawback of the frame-based model lies in its pursuit of optimal frame-level predictions rather than the best event-level ones. Moreover, it demands post-processing and is incapable of being trained in an entirely end-to-end fashion. Based on the above research status, this paper proposes an event-based transformer method -<b>R</b>espiratory<b>E</b>vents<b>D</b>etection<b>T</b>ransformer (REDT) for multi-class respiratory sound event detection task to achieve efficient recognition and localization of the respiratory phase and adventitious sound events.<i>Approach.</i>Firstly, REDT approach employs the Transformer for time-frequency analysis of respiratory sound signals to extract essential features. Secondly, REDT converts these features into timestamp representations and achieves sound event detection by predicting the location and category of timestamps.<i>Main results.</i>Our method is validated on the public dataset HF_Lung_V1. The experimental results show that our F1 scores for inspiration, expiration, continuous adventitious sound and discontinuous adventitious sound are 90.5%, 77.3%, 78.9%, and 59.4%, respectively.<i>Significance.</i>These results demonstrate the method's significant performance in respiratory sound event detection.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhysioEx: a new Python library for explainable sleep staging through deep learning.","authors":"Guido Gagliardi, Antonio Luca Alfeo, Mario G C A Cimino, Gaetano Valenza, Maarten De Vos","doi":"10.1088/1361-6579/adaf73","DOIUrl":"10.1088/1361-6579/adaf73","url":null,"abstract":"<p><p><i>Objective.</i>Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx (Physiological Signal Explainer), a Python library designed to support the analysis of sleep stages using deep learning (DL) and Explainable AI (XAI).<i>Approach.</i>PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability. It supports both low-resource devices and high-performance computing clusters and includes pretrained models based on the Sleep Heart Health Study dataset. These models support single-channel EEG and multichannel EEG-EOG-EMG configurations and are easily adaptable to custom datasets. PhysioEx also features a command-line interface toolbox allowing users to streamline the model development and deployment. The library offers a range of XAI post-hoc methods to explain model decisions and align them with expert knowledge.<i>Main results.</i>PhysioEx benchmark state-of-the-art sleep staging models in a standard pipeline. Enabling a fair comparison between them both on the training source and out-of-domain sources. Its XAI techniques provide insights into DL-based sleep staging by linking model decisions to human-understandable concepts, such as American Academy of Sleep Medicine-defined rules.<i>Significance.</i>PhysioEx addresses the need for a standardized and accessible platform for sleep staging analysis, combining DL and XAI. By supporting modular workflows and explainable insights, it bridges the gap between machine learning models and clinical expertise. PhysioEx is publicly available and installable via pip⁶6https://pypi.org/project/physioex/., making it a valuable tool for researchers and practitioners in sleep medicine.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}