Physiological measurement最新文献

{"title":"Correlation analysis of estimated pulse wave velocity and severe abdominal aortic calcification: based on the National Health and Nutrition Examination Survey database.","authors":"Guanghui Zhao, Zhiyu Guo, Peng Zheng","doi":"10.1088/1361-6579/ad9ce6","DOIUrl":"https://doi.org/10.1088/1361-6579/ad9ce6","url":null,"abstract":"<p><p><i>Objective.</i>To investigate how severe abdominal aortic calcification (SAAC) and estimated pulse wave velocity (ePWV) relate to each other and to all-cause and cardiovascular disease (CVD) mortalities.<i>Approach.</i>National Health and Nutrition Examination Survey 2013-2014 data were analyzed. ePWV, computed using age and mean blood pressure, served as an independent variable. Dependent variable SAAC (AAC score >6) was quantified using dual-energy x-ray absorptiometry and Kauppila grading. A weighted logistic regression model, interaction terms, and restricted cubic spline analysis examined relationship between ePWV and SAAC. Kaplan-Meier curves were drawn among SAAC people. A weighted Cox regression model was built to examine associations of ePWV with all-cause and CVD mortalities.<i>Main results.</i>2849 individuals were included. A strong positive connection (odds ratio (OR) > 1,<i>P</i>< 0.05) was seen between ePWV and SAAC risk. Interaction term<i>P</i>-value indicated that only ethnicity (<i>P</i>< 0.05) had an impact on this link but smoking, alcohol use, age, sex, body mass index, or hypertension did not. SAAC patients showed greater ePWV, all-cause and CVD mortalities (<i>P</i>< 0.05) than non-SAAC subjects. Greater ePWV (>12.00 m s^-1) was associated with higher risks of all-cause and CVD mortalities in SAAC participants (hazard ratio (HR) > 1,<i>P</i>< 0.05). Significance<i>.</i>This study, for the first time based on the NHANES database, reveals a positive correlation between ePWV and SAAC, and identifies ePWV as an independent predictor of all-cause and cardiovascular mortality risk in patients with SAAC, providing a new biomarker for the prevention and early intervention of cardiovascular diseases.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"45 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932605","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":"Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.","authors":"Serena Zanelli, Davide Agnoletti, Jordi Alastruey, John Allen, Elisabetta Bianchini, Vasiliki Bikia, Pierre Boutouyrie, Rosa Maria Bruno, Rachel Climie, Djammaleddine Djeldjli, Eugenia Gkaliagkousi, Alessandro Giudici, Kristina Gopcevic, Andrea Grillo, Andrea Guala, Bernhard Hametner, Jayaraj Joseph, Parmis Karimpour, Vimarsha Kodithuwakku, Panicos A Kyriacou, Antonios Lazaridis, Mai Tone Lønnebakken, Maria Raffaella Martina, Christopher Clemens Mayer, P M Nabeel, Petras Navickas, János Nemcsik, Stefan Orter, Chloe Park, Telmo Pereira, Giacomo Pucci, Ana Belen Amado Rey, Paolo Salvi, Ana Carolina Gonçalves Seabra, Ute Seeland, Thomas van Sloten, Bart Spronck, Gerard Stansby, Indra Steens, Thomas Stieglitz, Isabella Tan, Dave Veerasingham, Siegfried Wassertheurer, Thomas Weber, Berend E Westerhof, Peter H Charlton","doi":"10.1088/1361-6579/ad548e","DOIUrl":"10.1088/1361-6579/ad548e","url":null,"abstract":"<p><p>Vascular ageing (vascular ageing) is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261732","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":"Hemodynamic effects of bifurcation and stenosis geometry on carotid arteries with different degrees of stenosis.","authors":"Yuxin Guo, Jianbao Yang, Junzhen Xue, Jingxi Yang, Siyu Liu, XueLian Zhang, Yixin Yao, Anlong Quan, Yang Zhang","doi":"10.1088/1361-6579/ad9c13","DOIUrl":"10.1088/1361-6579/ad9c13","url":null,"abstract":"<p><p><i>Objective.</i>Carotid artery stenosis (CAS) is a key factor in pathological conditions, such as thrombosis, which is closely linked to hemodynamic parameters. Existing research often focuses on analyzing the influence of geometric characteristics at the stenosis site, making it difficult to predict the effects of overall vascular geometry on hemodynamic parameters. The objective of this study is to comprehensively examine the influence of geometric morphology at different degrees of CAS and at bifurcation sites on hemodynamic parameters.<i>Approach.</i>A three-dimensional model is established using computed tomography angiography images, and eight geometric parameters of each patient are measured by MIMICS. Then, computational fluid dynamics is utilized to investigate 60 patients with varying degrees of stenosis (10%-95%). Time and grid tests are conducted to optimize settings, and results are validated through comparison with reference calculations. Subsequently, correlation analysis using SPSS is performed to examine the relationship between the eight geometric parameters and four hemodynamic parameters. In MATLAB, prediction models for the four hemodynamic parameters are developed using back propagation neural networks (BPNN) and multiple linear regression.<i>Main results.</i>The BPNN model significantly outperforms the multiple linear regression model, reducing mean absolute error, mean squared error, and root mean squared error by 91.7%, 93.9%, and 75.5%, respectively, and increasing<i>R</i>²from 19.0% to 88.0%. This greatly improves fitting accuracy and reduces errors. This study elucidates the correlation and patterns of geometric parameters of vascular stenosis and bifurcation in evaluating hemodynamic parameters of CAS.<i>Significance.</i>This study opens up new avenues for improving the diagnosis, treatment, and clinical management strategies of CAS.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802007","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":"Artifact identification and removal methodologies for intracranial pressure signals: a systematic scoping review.","authors":"Tobias Bergmann, Nuray Vakitbilir, Alwyn Gomez, Abrar Islam, Kevin Y Stein, Amanjyot Singh Sainbhi, Noah Silvaggio, Izzy Marquez, Logan Froese, Frederick A Zeiler","doi":"10.1088/1361-6579/ad9af4","DOIUrl":"10.1088/1361-6579/ad9af4","url":null,"abstract":"<p><p><i>Objective</i>. Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management.<i>Methods.</i>A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals.<i>Results.</i>The search yielded 5875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method.<i>Conclusion.</i>There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness.<i>Significance</i>. This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785600","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":"Telephone training to improve ECG quality in remote screening for atrial fibrillation.","authors":"Kethaki Prathivadi Bhayankaram, Jonathan Mant, James Brimicombe, Andrew Dymond, Kate Williams, Peter H Charlton","doi":"10.1088/1361-6579/ad9798","DOIUrl":"10.1088/1361-6579/ad9798","url":null,"abstract":"<p><p><i>Objective.</i>Self-recorded, single-lead electrocardiograms (ECGs) are increasingly used to diagnose arrhythmias. However, they can be of variable quality, affecting the reliability of interpretation. In this analysis of ECGs collected in atrial fibrillation screening studies, our aims were to: (i) determine the quality of ECGs when recorded unsupervised; and (ii) investigate whether telephone training improved ECG quality.<i>Approach.</i>Data was obtained from the Screening for Atrial Fibrillation with ECG to Reduce stroke programme, where participants recorded four single-lead ECG traces per day for three weeks using a handheld device. ECG quality was assessed by an automated algorithm, and participants who recorded >25% poor-quality ECGs from days 4-10 of screening were identified for training to improve ECG recording technique. Training was delivered when research team capacity permitted.<i>Main results.</i>13 741 participants recorded 1127 264 ECGs, of which 41 288 (3.7%) were poor-quality. Most participants (51.5%) did not record any poor-quality ECGs. 1,088 (7.9%) participants met the threshold for training. Of these, 165 participants received training and 923 did not. The median proportion of poor-quality ECGs per participant on days 1-3 was 41.7 (27.3-50.0)% for those who received training and 33.3 (25.0-45.5)% for those who did not. On days 11-21, the median proportions of poor-quality ECGs per participant were significantly lower (<i>p</i>< 0.001) for those who received training, 17.8 (5.0-31.6)%, and those who did not, 14.0 (4.8-30.2)%. Comparing these groups, the mean (95% confidence interval) reduction in proportion of poor-quality ECGs from days 1-3 to days 11-21 was 20.2 (16.8-23.5)% in those who received training and 16.0 (14.7-17.3)% in those who did not (<i>p</i>= 0.396).<i>Significance.</i>Most participants achieved adequate quality ECGs. For those that did not, ECG quality improved over time regardless of whether they received telephone training. Telephone training may therefore not be required to achieve improvements in ECG quality during screening.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11651129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142731831","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":"Swing limb detection using a convolutional neural network and a sequential hypothesis test based on foot pressure data during gait initialization in individuals with Parkinson's disease.","authors":"Hsiao-Lung Chan, Ya-Ju Chang, Shih-Hsun Chien, Gia-Hao Fang, Cheng-Chung Kuo, Yi-Tao Chen, Rou-Shayn Chen","doi":"10.1088/1361-6579/ad9af5","DOIUrl":"https://doi.org/10.1088/1361-6579/ad9af5","url":null,"abstract":"<p><p><i>Objective</i>. Start hesitation is a key issue for individuals with Parkinson's disease (PD) during gait initiation. Visual cues have proven effective in enhancing gait initiation. When applied to laser-light shoes, swing-limb detection efficiently activates the laser on the side of the stance limb, prompting the opposite swing limb to initiate stepping.<i>Approach</i>. This paper presents the development of two models for this purpose: a convolutional neural network that predicts the swing limb's side using center of pressure data, and a swing onset detection model based on sequential hypothesis test using foot pressure data.<i>Main results</i>. Our findings demonstrate an accuracy rate of 85.4% in predicting the swing limb's side, with 82.4% of swing onsets correctly detected within 0.05 s.<i>Significance</i>. This study demonstrates the efficiency of swing-limb detection based on foot pressures. Future research aims to comprehensively assess the impact of this method on improving gait initiation in individuals with PD.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":"45 12","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829744","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":"Impact of macrohemodynamic manipulations during cardiopulmonary bypass on finger microcirculation assessed by photoplethysmography signal components.","authors":"Gerardo Tusman, Stephan H Böhm, Nora Fuentes, Cecilia M Acosta, Daniel Absi, Carlos Climente, Fernando Suarez Sipmann","doi":"10.1088/1361-6579/ad9af6","DOIUrl":"10.1088/1361-6579/ad9af6","url":null,"abstract":"<p><p><i>Objective.</i>Continuous monitoring of the hemodynamic coherence between macro and microcirculation is difficult at the bedside. We tested the role of photoplethysmography (PPG) to real-time assessment of microcirculation during extreme manipulation of macrohemodynamics induced by the cardiopulmonary bypass (CPB).<i>Approach.</i>We analyzed the alternating (AC) and direct (DC) components of the finger PPG in 12 patients undergoing cardiac surgery with CPB at five moments: (1) before-CPB; (2) CPB-start, at the transition from pulsatile to non-pulsatile blood flow; (3) CPB-aortic clamping, at a sudden decrease in pump blood flow and volemia.; (4) CPB-weaning, during step-wise 20% decreases in pump blood flow and opposite proportional increases in native pulsatile blood flow; and (5) after-CPB.<i>Main results.</i>Nine Caucasian men and three women were included for analysis. Macrohemodynamic changes during CPB had an immediate impact on the PPG at all studied moments. Before-CPB the AC signal amplitude showed a median and IQR values of 0.0023(0.0013). The AC signal completely disappeared at CPB-start and at CPB-aortic clamping. During CPB weaning its amplitude progressively increased but remained lower than before CPB, at 80% [0.0008 (0.0005);<i>p</i>< 0.001], 60% [0.0010(0.0006);<i>p</i>< 0.001], and 40% [0.0013(0.0009);<i>p</i>= 0.011] of CPB flow. The AC amplitude returned close to Before-CPB values at 20% of CPB flow [0.0015(0.0008);<i>p</i>= 0.081], when CPB was completely stopped [0.0019 (0.0009);<i>p</i>= 0.348], and at after-CPB [0.0021(0.0009);<i>p</i>= 0.687]. The DC signal Before-CPB [0.95(0.02)] did not differ statistically from CPB-start, CPB-weaning and After-CPB. However, at CPB-aortic clamping, at no flow and a sudden drop in volemia, the DC signal decreased from [0.96(0.01)] to [0.94(0.02);<i>p</i>= 0.002].<i>Significance.</i>The macrohemodynamic alterations brought on by CPB were consistent with changes in the finger's microcirculation. PPG described local pulsatile blood flow (AC) as well as non-pulsatile blood flow and volemia (DC) in the finger. These findings provide plausibility to the use of PPG in ongoing hemodynamic coherence monitoring.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785664","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":"openBF: an open-source finite volume 1D blood flow solver.","authors":"I Benemerito, A Melis, A Wehenkel, A Marzo","doi":"10.1088/1361-6579/ad9663","DOIUrl":"10.1088/1361-6579/ad9663","url":null,"abstract":"<p><p>Computational simulations are widely adopted in cardiovascular biomechanics because of their capability of producing physiological data otherwise impossible to measure with non-invasive modalities.<i>Objective.</i>This study presents openBF, a computational library for simulating the blood dynamics in the cardiovascular system.<i>Approach.</i>openBF adopts a one-dimensional viscoelastic representation of the arterial system, and is coupled with zero-dimensional windkessel models at the outlets. Equations are solved by means of the finite-volume method and the code is written in Julia. We assess its predictions by performing a multiscale validation study on several domains available from the literature.<i>Main results.</i>At all scales, which range from individual arteries to a population of virtual subjects, openBF's solution show excellent agreement with the solutions from existing software. For reported simulations, openBF requires low computational times.<i>Significance.</i>openBF is easy to install, use, and deploy on multiple platforms and architectures, and gives accurate prediction of blood dynamics in short time-frames. It is actively maintained and available open-source on GitHub, which favours contributions from the biomechanical community.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693074","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":"Convolution spatial-temporal attention network for EEG emotion recognition.","authors":"Lei Cao, Binlong Yu, Yilin Dong, Tianyu Liu, Jie Li","doi":"10.1088/1361-6579/ad9661","DOIUrl":"10.1088/1361-6579/ad9661","url":null,"abstract":"<p><p>In recent years, emotion recognition using electroencephalogram (EEG) signals has garnered significant interest due to its non-invasive nature and high temporal resolution. We introduced a groundbreaking method that bypasses traditional manual feature engineering, emphasizing data preprocessing and leveraging the topological relationships between channels to transform EEG signals from two-dimensional time sequences into three-dimensional spatio-temporal representations. Maximizing the potential of deep learning, our approach provides a data-driven and robust method for identifying emotional states. Leveraging the synergy between convolutional neural network and attention mechanisms facilitated automatic feature extraction and dynamic learning of inter-channel dependencies. Our method showcased remarkable performance in emotion recognition tasks, confirming the effectiveness of our approach, achieving average accuracy of 98.62% for arousal and 98.47% for valence, surpassing previous state-of-the-art results of 95.76% and 95.15%. Furthermore, we conducted a series of pivotal experiments that broadened the scope of emotion recognition research, exploring further possibilities in the field of emotion recognition.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142693142","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":"pyPCG: a Python toolbox specialized for phonocardiography analysis.","authors":"Kristof Müller, Janka Hatvani, Miklos Koller, Márton Áron Goda","doi":"10.1088/1361-6579/ad9af7","DOIUrl":"https://doi.org/10.1088/1361-6579/ad9af7","url":null,"abstract":"<p><strong>Objective: </strong>Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. The development of methods which analyse phonocardiographic data tries to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. In these approaches there is little to no standardization and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography.</p><p><strong>Approach: </strong>We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps and with these, complex analysis pipelines can be created. These functions can be tested individually.</p><p><strong>Main results: </strong>Due to the interdependence of the steps, we validated the current segmentation stage using two phonocardiogram datasets, a fetal dataset comprising 50 one-minute abdominal PCG recordings, which include 6758 S1 and 6729 S2 labels and a filtered version of the dataset used in the 2022 PhysioNet Challenge, containing 413 records with 9795 S1 and 9761 S2 labels. Our results were compared to other common and publicly available segmentation methods, such as peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. Our best model achieved a 96.1% F1 score and 11.7 ms mean absolute error for fetal S1 detection, and 81.3% F1 score and 50.5 ms mean absolute error for PhysioNet S1 detection.</p><p><strong>Significance: </strong>Our detection method outperformed all other tested methods on the fetal dataset and&#xD;achieved results comparable to the state of the art on the PhysioNet dataset. Accurate segmentation of signals is critical for the calculation of accurate statistical measures and the creation of classification models. Our toolbox contains functions for both feature extraction and calculation of statistics which are compatible with the previous steps. All of our methods can be fine tuned for specific datasets. pyPCG is available on https://pypcg-toolbox.readthedocs.io/en/latest/.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785704","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}