Physiological measurement最新文献

{"title":"ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.","authors":"Adele Mirzaee Moghaddam Kasmaee, Alireza Ataei, Seyed Vahid Moravvej, Roohallah Alizadehsani, Juan M Gorriz, Yu-Dong Zhang, Ru-San Tan, U Rajendra Acharya","doi":"10.1088/1361-6579/ad46e2","DOIUrl":"10.1088/1361-6579/ad46e2","url":null,"abstract":"<p><p><i>Objective.</i>Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities.<i>Approach.</i>This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the artificial bee colony (ABC) algorithm to enhance the starting point for learning. An array of convolutional neural networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process.<i>Main results.</i>ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2% and a geometric mean of 90.6%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs.<i>Significance.</i>The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140870308","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":"Age-dependent coupling characteristics of bilateral frontal EEG during desflurane anesthesia.","authors":"Ziyang Li, Peiqi Wang, Licheng Han, Xinyu Hao, Weidong Mi, Li Tong, Zhenhu Liang","doi":"10.1088/1361-6579/ad46e0","DOIUrl":"10.1088/1361-6579/ad46e0","url":null,"abstract":"<p><p><i>Objectives.</i>The purpose of this study is to investigate the age dependence of bilateral frontal electroencephalogram (EEG) coupling characteristics, and find potential age-independent depth of anesthesia monitoring indicators for the elderlies.<i>Approach.</i>We recorded bilateral forehead EEG data from 41 patients (ranged in 19-82 years old), and separated into three age groups: 18-40 years (<i>n</i>= 12); 40-65 years (<i>n</i>= 14), >65 years (<i>n</i>= 15). All these patients underwent desflurane maintained general anesthesia (GA). We analyzed the age-related EEG spectra, phase amplitude coupling (PAC), coherence and phase lag index (PLI) of EEG data in the states of awake, GA, and recovery.<i>Main results.</i>The frontal alpha power shows age dependence in the state of GA maintained by desflurane. Modulation index in slow oscillation-alpha and delta-alpha bands showed age dependence and state dependence in varying degrees, the PAC pattern also became less pronounced with increasing age. In the awake state, the coherence in delta, theta and alpha frequency bands were all significantly higher in the >65 years age group than in the 18-40 years age group (<i>p</i>< 0.05 for three frequency bands). The coherence in alpha-band was significantly enhanced in all age groups in GA (<i>p</i>< 0.01) and then decreased in recovery state. Notably, the PLI in the alpha band was able to significantly distinguish the three states of awake, GA and recovery (<i>p</i>< 0.01) and the results of PLI in delta and theta frequency bands had similar changes to those of coherence.<i>Significance.</i>We found the EEG coupling and synchronization between bilateral forehead are age-dependent. The PAC, coherence and PLI portray this age-dependence. The PLI and coherence based on bilateral frontal EEG functional connectivity measures and PAC based on frontal single-channel are closely associated with anesthesia-induced unconsciousness.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140865101","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":"Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings.","authors":"Katharina M Jaeger, Michael Nissen, Simone Rahm, Adriana Titzmann, Peter A Fasching, Janina Beilner, Bjoern M Eskofier, Heike Leutheuser","doi":"10.1088/1361-6579/ad4952","DOIUrl":"10.1088/1361-6579/ad4952","url":null,"abstract":"<p><p><i>Objective.</i>Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.<i>Approach.</i>In this work, we propose<i>Power-MF</i>, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark<i>Power-MF</i>against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).<i>Main results.</i>Our results show that<i>Power-MF</i>outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.<i>Significance.</i>Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899223","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":"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 (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.<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 SpO₂and TcPO₂(<i>p</i>< 0.01). OSA severity affected the response delay of SpO₂, TcPO₂, 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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}