Objective.Intermittent hypoxia, the primary pathology of obstructive sleep apnea (OSA), causes cardiovascular responses resulting in changes in hemodynamic parameters such as stroke volume (SV), blood pressure (BP), and heart rate (HR). However, previous studies have produced very different conclusions, such as suggesting that SV increases or decreases during apnea. A key reason for drawing contrary conclusions from similar measurements may be due to ignoring the time delay in acquiring response signals. By analyzing the signals collected during hypoxia, we aim to establish criteria for determining the delay time between the onset of apnea and the onset of physiological parameter response.Approach.We monitored oxygen saturation (SpO2), transcutaneous oxygen pressure (TcPO2), and hemodynamic parameters SV, HR, and BP, during sleep in 66 patients with different OSA severity to observe body's response to hypoxia and determine the delay time of above parameters. Data were analyzed using the Kruskal-Wallis test, Quade test, and Spearman test.Main results.We found that simultaneous acquisition of various parameters inevitably involved varying degrees of response delay (7.12-25.60 s). The delay time of hemodynamic parameters was significantly shorter than that of SpO2and TcPO2(p< 0.01). OSA severity affected the response delay of SpO2, TcPO2, SV, mean BP, and HR (p< 0.05). SV delay time was negatively correlated with the apnea-hypopnea index (r= -0.4831,p< 0.0001).Significance.The real body response should be determined after removing the effect of delay time, which is the key to solve the problem of drawing contradictory conclusions from similar studies. The methods and important findings presented in this study provide key information for revealing the true response of the cardiovascular system during hypoxia, indicating the importance of proper signal analysis for correctly interpreting the cardiovascular hemodynamic response phenomena and exploring their physiological and pathophysiological mechanisms.
{"title":"Time delays between physiological signals in interpreting the body's responses to intermittent hypoxia in obstructive sleep apnea.","authors":"Geng Li, Mengwei Zhou, Xiaoqing Huang, Changjin Ji, Tingting Fan, Jinkun Xu, Huahui Xiong, Yaqi Huang","doi":"10.1088/1361-6579/ad45ac","DOIUrl":"10.1088/1361-6579/ad45ac","url":null,"abstract":"<p><p><i>Objective.</i>Intermittent hypoxia, the primary pathology of obstructive sleep apnea (OSA), causes cardiovascular responses resulting in changes in hemodynamic parameters such as stroke volume (SV), blood pressure (BP), and heart rate (HR). However, previous studies have produced very different conclusions, such as suggesting that SV increases or decreases during apnea. A key reason for drawing contrary conclusions from similar measurements may be due to ignoring the time delay in acquiring response signals. By analyzing the signals collected during hypoxia, we aim to establish criteria for determining the delay time between the onset of apnea and the onset of physiological parameter response.<i>Approach.</i>We monitored oxygen saturation (SpO<sub>2</sub>), transcutaneous oxygen pressure (TcPO<sub>2</sub>), and hemodynamic parameters SV, HR, and BP, during sleep in 66 patients with different OSA severity to observe body's response to hypoxia and determine the delay time of above parameters. Data were analyzed using the Kruskal-Wallis test, Quade test, and Spearman test.<i>Main results.</i>We found that simultaneous acquisition of various parameters inevitably involved varying degrees of response delay (7.12-25.60 s). The delay time of hemodynamic parameters was significantly shorter than that of SpO<sub>2</sub>and TcPO<sub>2</sub>(<i>p</i>< 0.01). OSA severity affected the response delay of SpO<sub>2</sub>, TcPO<sub>2</sub>, SV, mean BP, and HR (<i>p</i>< 0.05). SV delay time was negatively correlated with the apnea-hypopnea index (<i>r</i>= -0.4831,<i>p</i>< 0.0001).<i>Significance.</i>The real body response should be determined after removing the effect of delay time, which is the key to solve the problem of drawing contradictory conclusions from similar studies. The methods and important findings presented in this study provide key information for revealing the true response of the cardiovascular system during hypoxia, indicating the importance of proper signal analysis for correctly interpreting the cardiovascular hemodynamic response phenomena and exploring their physiological and pathophysiological mechanisms.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868611","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}
Pub Date : 2024-05-02DOI: 10.1088/1361-6579/ad3d28
Jonathan Fhima, Jan Van Eijgen, Marie-Isaline Billen Moulin-Romsée, Heloïse Brackenier, Hana Kulenovic, Valérie Debeuf, Marie Vangilbergen, Moti Freiman, Ingeborg Stalmans and Joachim A Behar
Objective. This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health. Approach. We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation. Main Results. LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators. Significance. The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.
{"title":"LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images","authors":"Jonathan Fhima, Jan Van Eijgen, Marie-Isaline Billen Moulin-Romsée, Heloïse Brackenier, Hana Kulenovic, Valérie Debeuf, Marie Vangilbergen, Moti Freiman, Ingeborg Stalmans and Joachim A Behar","doi":"10.1088/1361-6579/ad3d28","DOIUrl":"https://doi.org/10.1088/1361-6579/ad3d28","url":null,"abstract":"Objective. This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health. Approach. We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation. Main Results. LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators. Significance. The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840831","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}
Pub Date : 2024-04-25DOI: 10.1088/1361-6579/ad43ad
Nürfet Balkan, Mustafa Cavusoglu, René Hornung
The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disorders breathing in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore sleep disordered breathing during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard for in diagnostics is overnight polysomnography involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes. The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight polysomnography for pregnant women were evaluated. An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes. Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health. .
{"title":"Application of portable sleep monitoring devices in pregnancy: a comprehensive review.","authors":"Nürfet Balkan, Mustafa Cavusoglu, René Hornung","doi":"10.1088/1361-6579/ad43ad","DOIUrl":"https://doi.org/10.1088/1361-6579/ad43ad","url":null,"abstract":"The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disorders breathing in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore sleep disordered breathing during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard for in diagnostics is overnight polysomnography involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes. The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight polysomnography for pregnant women were evaluated. An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes. Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health. .","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653217","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}
Pub Date : 2024-04-25DOI: 10.1088/1361-6579/ad43ae
Yangcheng Huang, Mingjie Wang, Yi-Gang Li, Wenjie Cai
OBJECTIVE Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Although lead misplacement is a relatively low-probability event, with an incidence ranging from 0.4% to 4%, the large number of ECG records in clinical practice necessitates the development of an effective detection method. This paper aimed to address this gap by presenting a novel lead misplacement detection method based on deep learning models. APPROACH We developed two novel lightweight deep learning model for limb and chest lead misplacement detection, respectively. For limb lead misplacement detection, two limb leads and V6 were used as inputs, while for chest lead misplacement detection, six chest leads were used as inputs. Our models were trained and validated using the Chapman database, with an 8:2 train-validation split, and evaluated on the PTB-XL, PTB, and LUDB databases. Additionally, we examined the model interpretability on the LUDB databases. Limb lead misplacement simulations were performed using mathematical transformations, while chest lead misplacement scenarios were simulated by interchanging pairs of leads. The detection performance was assessed using metrics such as accuracy, precision, sensitivity, specificity, and Macro F1-score. MAIN RESULTS Our experiments simulated three scenarios of limb lead misplacement and nine scenarios of chest lead misplacement. The proposed two models achieved Macro F1-scores ranging from 93.42% to 99.61% on two heterogeneous test sets, demonstrating their effectiveness in accurately detecting lead misplacement across various arrhythmias. SIGNIFICANCE The significance of this study lies in providing a reliable open-source algorithm for lead misplacement detection in ECG recordings. The source code is available at https://github.com/wjcai/ECG_lead_check.
{"title":"A lightweight deep learning approach for detecting electrocardiographic lead misplacement.","authors":"Yangcheng Huang, Mingjie Wang, Yi-Gang Li, Wenjie Cai","doi":"10.1088/1361-6579/ad43ae","DOIUrl":"https://doi.org/10.1088/1361-6579/ad43ae","url":null,"abstract":"OBJECTIVE\u0000Electrocardiographic (ECG) lead misplacement can result in distorted waveforms and amplitudes, significantly impacting accurate interpretation. Although lead misplacement is a relatively low-probability event, with an incidence ranging from 0.4% to 4%, the large number of ECG records in clinical practice necessitates the development of an effective detection method. This paper aimed to address this gap by presenting a novel lead misplacement detection method based on deep learning models.\u0000\u0000\u0000APPROACH\u0000We developed two novel lightweight deep learning model for limb and chest lead misplacement detection, respectively. For limb lead misplacement detection, two limb leads and V6 were used as inputs, while for chest lead misplacement detection, six chest leads were used as inputs. Our models were trained and validated using the Chapman database, with an 8:2 train-validation split, and evaluated on the PTB-XL, PTB, and LUDB databases. Additionally, we examined the model interpretability on the LUDB databases. Limb lead misplacement simulations were performed using mathematical transformations, while chest lead misplacement scenarios were simulated by interchanging pairs of leads. The detection performance was assessed using metrics such as accuracy, precision, sensitivity, specificity, and Macro F1-score.\u0000\u0000\u0000MAIN RESULTS\u0000Our experiments simulated three scenarios of limb lead misplacement and nine scenarios of chest lead misplacement. The proposed two models achieved Macro F1-scores ranging from 93.42% to 99.61% on two heterogeneous test sets, demonstrating their effectiveness in accurately detecting lead misplacement across various arrhythmias.\u0000\u0000\u0000SIGNIFICANCE\u0000The significance of this study lies in providing a reliable open-source algorithm for lead misplacement detection in ECG recordings. The source code is available at https://github.com/wjcai/ECG_lead_check.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654714","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}
OBJECTIVE The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous ECG and PCG recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels. Approach: We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics. Main Results: The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis. Significance: The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.
{"title":"An open-access simultaneous electrocardiogram and phonocardiogram database.","authors":"Arsalan Kazemnejad, Sajjad Karimi, Peiman Gordany, Gari D. Clifford, Reza Sameni","doi":"10.1088/1361-6579/ad43af","DOIUrl":"https://doi.org/10.1088/1361-6579/ad43af","url":null,"abstract":"OBJECTIVE\u0000The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous ECG and PCG recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels. Approach: We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics. Main Results: The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis. Significance: The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653584","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}
Pub Date : 2024-04-23DOI: 10.1088/1361-6579/ad4251
Hans van Gorp, M. V. van Gilst, S. Overeem, Sylvie Dujardin, Angelique Pijpers, Bregje van Wetten, Pedro Fonseca, Ruud J. G. van Sloun
OBJECTIVE Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders. Approach. We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram. Main results. For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without. Significance. The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.
{"title":"Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.","authors":"Hans van Gorp, M. V. van Gilst, S. Overeem, Sylvie Dujardin, Angelique Pijpers, Bregje van Wetten, Pedro Fonseca, Ruud J. G. van Sloun","doi":"10.1088/1361-6579/ad4251","DOIUrl":"https://doi.org/10.1088/1361-6579/ad4251","url":null,"abstract":"OBJECTIVE\u0000Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders. Approach. We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram. Main results. For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without. Significance. The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670261","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}
Pub Date : 2024-04-17DOI: 10.1088/1361-6579/ad37ee
Cheng Ding, Ran Xiao, Weijia Wang, Elizabeth Holdsworth, Xiao Hu
Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.
{"title":"Photoplethysmography based atrial fibrillation detection: a continually growing field.","authors":"Cheng Ding, Ran Xiao, Weijia Wang, Elizabeth Holdsworth, Xiao Hu","doi":"10.1088/1361-6579/ad37ee","DOIUrl":"10.1088/1361-6579/ad37ee","url":null,"abstract":"<p><p><i>Objective.</i> Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.<i>Approach.</i> This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.<i>Significance.</i> Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288769","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}
Pub Date : 2024-04-16DOI: 10.1088/1361-6579/ad3458
Claire C Onsager, Chulin Wang, Charles Costakis, Can C Aygen, Lauren Lang, Suzan van der Lee, Matthew A Grayson
Objective. Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols. Approach. Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements. Main result. The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts. Significance. The reduction in model space dimension is shown to increase computational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivity tolerates higher noise levels up to several orders of magnitude larger than standard methods.
目的。电阻抗层析成像（EIT）是一种无创成像方法，通过对异质导体外围的电测量值进行反演，绘制其内部电导率图。本文提出的 EIT 方法旨在通过引入灵敏度体积作为比较 EIT 测量方案的优劣势，从而提高计算速度和噪声容限。方法。每个测量值都对应模型空间中的一个灵敏度向量，因此测量值集合又对应模型空间中一个灵敏度体积的向量集合。最大灵敏度体积确定了具有最大灵敏度和最大相互正交性的测量协议。对可区分性标准进行了概括，以量化高灵敏度测量所增加的噪声容限。主要结果。灵敏度体积法可以最小化模型空间维度，使其与数据空间维度相匹配，并在由更多接触点定义的扩展测量空间内提高数据的重要性。意义重大。模型空间维度的缩小提高了计算效率，使层析反演的速度加快了几个数量级，而灵敏度的提高可容忍比标准方法大几个数量级的更高噪声水平。
{"title":"Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography","authors":"Claire C Onsager, Chulin Wang, Charles Costakis, Can C Aygen, Lauren Lang, Suzan van der Lee, Matthew A Grayson","doi":"10.1088/1361-6579/ad3458","DOIUrl":"https://doi.org/10.1088/1361-6579/ad3458","url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols. <italic toggle=\"yes\">Approach.</italic> Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements. <italic toggle=\"yes\">Main result.</italic> The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts. <italic toggle=\"yes\">Significance.</italic> The reduction in model space dimension is shown to increase <italic toggle=\"yes\">computational efficiency</italic>, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivity <italic toggle=\"yes\">tolerates higher noise</italic> levels up to several orders of magnitude larger than standard methods.","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140613520","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}
Pub Date : 2024-04-16DOI: 10.1088/1361-6579/ad33a1
Lin Yang, Zhijun Gao, Xinsheng Cao, Chunchen Wang, Hang Wang, Jing Dai, Yang Liu, Yilong Qin, Meng Dai, Binghua Zhang, Ke Zhao, Zhanqi Zhao
Objective. This study aims to explore the possibility of using electrical impedance tomography (EIT) to assess pursed lips breathing (PLB) performance of patients with chronic obstructive pulmonary disease (COPD).Methods. 32 patients with COPD were assigned equally to either the conventional group or the EIT guided group. All patients were taught to perform PLB by a physiotherapist without EIT in the conventional group or with EIT in the EIT guided group for 10 min. The ventilation of all patients in the final test were continuously monitored using EIT and the PLB performances were rated by another physiotherapist before and after reviewing EIT. The global and regional ventilation between two groups as well as between quite breathing (QB) and PLB were compared and rating scores with and without EIT were also compared.Results.For global ventilation, the inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB was significantly larger than those during QB for both group (P< 0.001). The inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB in the EIT guided group were higher compared to those in the conventional group (P< 0.001), as well as expiratory flow expiratory uniformity and respiratory stability were better (P< 0.001). For regional ventilation, center of ventilation significantly decreased during PLB (P< 0.05). The expiratory time constant during PLB in the EIT guided group was greater than that in the conventional group (P< 0.001). Additionally, Bland-Altman plots analysis suggested a high concordance between subjective rating and rating with the help of EIT, but the score rated after EIT observation significantly lower than that rated subjectively in both groups (score drop of -2.68 ± 1.1 in the conventional group and -1.19 ± 0.72 in the EIT guided group,P< 0.01).Conclusion.EIT could capture the details of PLB maneuver, which might be a potential tool to quantitatively evaluate PLB performance and thus assist physiotherapists to teach PLB maneuver to patients.
目的：
本研究旨在探讨使用电阻抗断层扫描（EIT）评估慢性阻塞性肺病患者做肺活量测定的可能性。在传统组中，所有患者均由物理治疗师在不使用 EIT 的情况下教其进行 PLB；在 EIT 指导组中，所有患者均由物理治疗师在使用 EIT 的情况下教其进行 PLB，时间为 10 分钟。在最后的测试中，所有患者的通气情况均由 EIT 持续监测，并由另一名物理治疗师在复查 EIT 前后对 PLB 表演进行评分。比较了两组之间以及相当呼吸（QB）和 PLB 之间的整体和区域通气情况，还比较了使用 EIT 和未使用 EIT 的评分。
{"title":"Visualizing pursed lips breathing of patients with chronic obstructive pulmonary disease through evaluation of global and regional ventilation using electrical impedance tomography.","authors":"Lin Yang, Zhijun Gao, Xinsheng Cao, Chunchen Wang, Hang Wang, Jing Dai, Yang Liu, Yilong Qin, Meng Dai, Binghua Zhang, Ke Zhao, Zhanqi Zhao","doi":"10.1088/1361-6579/ad33a1","DOIUrl":"10.1088/1361-6579/ad33a1","url":null,"abstract":"<p><p><i>Objective</i>. This study aims to explore the possibility of using electrical impedance tomography (EIT) to assess pursed lips breathing (PLB) performance of patients with chronic obstructive pulmonary disease (COPD).<i>Methods</i>. 32 patients with COPD were assigned equally to either the conventional group or the EIT guided group. All patients were taught to perform PLB by a physiotherapist without EIT in the conventional group or with EIT in the EIT guided group for 10 min. The ventilation of all patients in the final test were continuously monitored using EIT and the PLB performances were rated by another physiotherapist before and after reviewing EIT. The global and regional ventilation between two groups as well as between quite breathing (QB) and PLB were compared and rating scores with and without EIT were also compared.<i>Results.</i>For global ventilation, the inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB was significantly larger than those during QB for both group (<i>P</i>< 0.001). The inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB in the EIT guided group were higher compared to those in the conventional group (<i>P</i>< 0.001), as well as expiratory flow expiratory uniformity and respiratory stability were better (<i>P</i>< 0.001). For regional ventilation, center of ventilation significantly decreased during PLB (<i>P</i>< 0.05). The expiratory time constant during PLB in the EIT guided group was greater than that in the conventional group (<i>P</i>< 0.001). Additionally, Bland-Altman plots analysis suggested a high concordance between subjective rating and rating with the help of EIT, but the score rated after EIT observation significantly lower than that rated subjectively in both groups (score drop of -2.68 ± 1.1 in the conventional group and -1.19 ± 0.72 in the EIT guided group,<i>P</i>< 0.01).<i>Conclusion.</i>EIT could capture the details of PLB maneuver, which might be a potential tool to quantitatively evaluate PLB performance and thus assist physiotherapists to teach PLB maneuver to patients.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140120290","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}
Pub Date : 2024-04-11DOI: 10.1088/1361-6579/ad37ed
Benjamin D Boudreaux, Ginny M Frederick, Patrick J O'Connor, Ellen M Evans, Michael D Schmidt
Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.
{"title":"Harmonization of three different accelerometers to classify the 24 h activity cycle.","authors":"Benjamin D Boudreaux, Ginny M Frederick, Patrick J O'Connor, Ellen M Evans, Michael D Schmidt","doi":"10.1088/1361-6579/ad37ed","DOIUrl":"10.1088/1361-6579/ad37ed","url":null,"abstract":"<p><p>Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.<i>Objective</i>. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.<i>Approach</i>. Participants (<i>n</i>= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.<i>Main results</i>. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.<i>Significance</i>. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140288768","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}
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