Physiological measurement最新文献

{"title":"Camera-based cardio-respiratory monitoring across the full fitness cycle.","authors":"Chang Xiao, Chengyifeng Tan, Lixia Song, Hongzhou Lu, Wenjin Wang","doi":"10.1088/1361-6579/adc364","DOIUrl":"10.1088/1361-6579/adc364","url":null,"abstract":"<p><p><i>Objective</i>. Exercise monitoring provides valuable insights into the cardio-respiratory health and fitness performance of exercisers. To address the limitations of existing studies that only monitors specific phases of the fitness cycle, this study introduces a novel approach for camera-based monitoring throughout the entire fitness cycle, encompassing the pre-exercise, during-exercise, and post-exercise phases.<i>Approach</i>. Validated video-based algorithms were employed to monitor physiological parameters, including heart rate (HR), HR variability (HRV) (time-domain, frequency-domain and nonlinear-domain metrics), and respiratory rate (RR). Measurements were conducted using a camera positioned in front of a treadmill, along with electrocardiogram (ECG), PPG recorded simultaneously for benchmarking. This work comprised of a total of 36 adult subjects (18 males, 18 females; average age: 21.3 ± 2.8 years), which are categorized into subjects with regular exercise habits (ES) and those without (NS) (ES: 10, NS: 26) based on their performance of this running trial organized in our study.<i>Main results</i>. The results showed that the camera-based system performed well in HR, RR and HRV measurement. In the pre-exercise phase, camera-based monitoring achieved an mean absolute error of 2.74 bpm for RR and 12.19 bpm for HR. HRV parameters, including mean interbeat interval and very low frequency, showed Pearson correlation coefficients of 0.99 and 0.97, respectively, with ECG. Compared to NS, ES exhibited more robust cardio-respiratory functioning, characterized by lower HR during exercise and faster HR recovery during post-exercise. Camera-based monitoring effectively captured these differences in physiological parameters across the fitness cycle.<i>Significance</i>. This study validates the feasibility and effectiveness of camera-based monitoring throughout the full fitness cycle. The findings highlight the contrasting cardio-respiratory responses between ES and NS, emphasizing the potential of camera-based systems in providing comprehensive, non-invasive insights into exercisers' fitness performance and cardiovascular health.The source code and dataset will be made open-source upon the acceptance at this sitehttps://github.com/contactless-healthcare/Camera-based-Monitoring-for-Full-Fitness-Cycle.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670601","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":"Extreme variability of vascular responses to slightly different abduction angles during abduction and external rotation tests, in patients with suspected thoracic outlet syndrome.","authors":"Simon Lecoq, Quentin Petit, Nathan Cronier, Samir Henni, Benedicte Noury, Pierre Abraham","doi":"10.1088/1361-6579/adc239","DOIUrl":"10.1088/1361-6579/adc239","url":null,"abstract":"<p><p><i>Objective.</i>Patients may not always perform a perfect 90° upper limb abduction when doing an abduction, external rotation test for the evaluation of thoracic outlet syndrome (TOS). We aimed to study the vascular responses to three slightly different abduction angles.<i>Approach.</i>We recorded fingertip arterial (A-PPG) and forearm venous (V-PPG) photo-plethysmography in 111 patients referred for suspicion or follow up of TOS. The measurements were made bilaterally during a 30 s surrender position, followed by moving elbows in the frontal plane without changing elbow and hand level to open the costo-clavicular angle (prayer position) to standardize venous results, either: slightly below (<90°), at the same level of (∼90°), or slightly above (>90°) the shoulder level, in a random order.<i>Main results.</i>With abnormal results defined as A-PPG <5%rest and V-PPG < 70%max in the surrender position, 54 of the 222 upper limbs were normal at all three tests. The proportion of abnormal tests decreased with the increase in abduction angle (Cochran<i>Q</i>< 0.05), 135 upper limbs showed impaired venous outflow for one (<i>n</i>= 74), two (<i>n</i>= 47) or the three angles (<i>n</i>= 14) without arterial inflow impairment at any of the three tests.<i>Significance.</i>Slight changes from a 'perfect' 90° abduction angle gave unreliable results during elevation, abduction, external rotation stress tests. A venous outflow impairment should probably be considered a physiologic response at <90° abduction.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657173","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":"Craniospinal compliance depends on the frequency of volume input.","authors":"Adam I Pelah, Magdalena Kasprowicz, Agnieszka Kazimierska, Ananya Chakravorty, Matthias Jaeger, Georgios Varsos, Marek Czosnyka, Zofia Czosnyka","doi":"10.1088/1361-6579/adc365","DOIUrl":"10.1088/1361-6579/adc365","url":null,"abstract":"<p><p><i>Objective.</i>Craniospinal compliance (CC) refers to the ability to maintain stable intracranial pressure (ICP) given changes in intracranial volume. CC can be calculated directly as the change in intracranial volume over change in ICP (Δ<i>V</i>/ΔICP). Considering the distinct spectral components of the ICP signal, it is pertinent to explore whether compliance is dependent on the frequency at which it is calculated.<i>Approach.</i>Data from 92 hydrocephalus patients who underwent computerized infusion studies was retrospectively analysed. ICP was recorded via lumbar puncture and cerebral blood flow velocity (CBFV) using transcranial Doppler ultrasonography. Compliance was calculated as Δ<i>V</i>/ΔICP, where<i>V</i>is cerebral arterial blood volume (CaBV), estimated by integrating CBFV over time. Compliance was calculated across three ICP wave frequencies: vasogenic<i>B</i>-waves, respiratory<i>R</i>-waves, and pulsatile waves.<i>Main results.</i>Compliances were significantly different (<i>p</i>< 0.001) across frequencies, and moderately correlated (<i>r</i>= 0.52 to<i>r</i>= 0.66), during baseline and plateau phases of the infusion study. Compliance decreased significantly from baseline to plateau (<i>p</i>< 0.001).<i>B</i>-wave CaBV amplitude was significantly higher than all other frequencies during both phases (<i>p</i>< 0.001), while pulsatile ICP amplitude was highest at baseline (<i>p</i>< 0.01), but tied with<i>B</i>-wave ICP amplitude during plateau (<i>p</i>= 0.10).<i>Significance.</i>The results support the notion that compliance is dependent on frequency, with higher compliances at slower frequencies. Where compliance is calculated in a clinical context, in hydrocephalus and traumatic brain injury, frequency should be considered for accurate results. Further research should explore this in a larger cohort, and in additional pathologies.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670604","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":"Machine learning-based non-invasive continuous dynamic monitoring of human core temperature with wearable dual temperature sensors.","authors":"Haotian Liang, Yishan Wang, Linbo Jiang, Xinming Yu, Linghao Xiong, Liang Luo, Le Fu, Yu Zhang, Ye Li, Jinzhong Song, Fangmin Sun","doi":"10.1088/1361-6579/adbf64","DOIUrl":"10.1088/1361-6579/adbf64","url":null,"abstract":"<p><p><i>Objective.</i>Due to the growing demand for personal health monitoring in extreme environments, continuous monitoring of core temperature has become increasingly important. Traditional monitoring methods, such as mercury thermometers and infrared thermometers, may have limitations in tracking real-time fluctuations in core temperature, especially in special application scenarios such as firefighting, military, and aerospace. This study aims to develop a non-invasive, continuous core temperature prediction model based on machine learning, addressing the limitations of traditional methods in extreme environments.<i>Approach.</i>This study develops a novel machine learning-based non-invasive continuous body core temperature monitoring model. A wearable dual temperature sensing device is designed to collect skin and environment temperature, six machine learning algorithms are trained utilizing data from 62 subjects.<i>Main results.</i>Performance evaluations on a test set of 10 subjects reveal outstanding results, achieving a mean absolute error of 0.15 °C ± 0.04 °C, a root mean square error of 0.17 °C ± 0.05 °C, and a mean absolute percentage error of 0.40% ± 0.12%. Statistical analysis further confirms the model's superior predictive capability compared to traditional methods.<i>Significance.</i>The developed temperature monitoring model not only provides enhanced accuracy in various conditions but also serves as a robust tool for individual health monitoring. This innovation is particularly significant in scenarios requiring continuous and precise temperature tracking, and offering entirely new insights for improved health management strategies and outcomes.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606095","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":"In-water electrical impedance tomography: EIT and the sea.","authors":"Andy Adler, Tarek El Harake, Martina Mosing, Andreas Fahlman","doi":"10.1088/1361-6579/adb82c","DOIUrl":"10.1088/1361-6579/adb82c","url":null,"abstract":"<p><p><i>Objective.</i>Electrical impedance tomography (EIT) has shown the ability to provide clinically useful functional information on ventilation in humans and other land mammals. We are motivated to use EIT with sea mammals and human divers, since EIT could provide unique information on lung ventilation that can help address diver performance and safety, and veterinary and behavioral questions. However, in-water use of EIT is challenging, primarily because sea water is more conductive than the body.<i>Approach.</i>We first address this issue by modeling the in-water component and evaluating image reconstruction algorithms.<i>Main results.</i>EIT is able to produce reasonable images if an outer insulating layer allows a water layer thickness <2% of the body radius. We next describe the design of custom EIT belts with an outer neoprene insulator to minimize current leakage. We show example underwater EIT recordings in human and dolphin subjects.<i>Significance.</i>We demonstrate in-water EIT is feasible with appropriate techniques.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143458997","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":"Parallel convolutional neural networks for non-invasive cardiac hemodynamic estimation: integrating uncalibrated PPG signals with nonlinear feature analysis.","authors":"Eugenia Ipar, Leandro J Cymberknop, Ricardo L Armentano","doi":"10.1088/1361-6579/adc366","DOIUrl":"10.1088/1361-6579/adc366","url":null,"abstract":"<p><p><i>Objective.</i>Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health assessment, as it is governed by key parameters such as cardiac output (CO), systemic vascular resistance (SVR), and arterial compliance (AC). This study aims to develop a non-invasive method using digital photoplethysmography (PPGD) signals and deep learning techniques to predict these biomarkers for a comprehensive CHS evaluation.<i>Approach.</i>A dataset of 4374 virtual subjects was used. Nonlinear features were extracted from PPGD signals to capture their inherent complexity and irregularity. A parallel convolutional neural network (PCNN) was implemented to process both raw signals and nonlinear features concurrently. Model performance was evaluated using<i>R</i>², root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE).<i>Main results.</i>The PCNN demonstrated satisfactory predictive performance with<i>R</i>², RMSE, MSE, and MAE values of 0.872, 0.086, 0.008, and 0.068 for CO; 0.851, 0.074, 0.006, and 0.058 for SVR; and 0.938, 0.049, 0.003, and 0.038 for AC. The proposed PCNN-based method offers a novel, non-invasive approach for predicting key cardiovascular biomarkers, providing an accurate CHS assessment.<i>Significance.</i>This method advances non-invasive cardiovascular diagnostics by combining PPGD signals and deep learning. Future work will focus on validating this findings in real-world settings for improved clinical applicability.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670607","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":"Noninvasive assessment of temporal dynamics in sympathetic and parasympathetic baroreflex responses.","authors":"Heberto Suarez-Roca, Negmeldeen Mamoun, Joseph P Mathew, Andrey V Bortsov","doi":"10.1088/1361-6579/adc23a","DOIUrl":"10.1088/1361-6579/adc23a","url":null,"abstract":"<p><p><i>Objective.</i>The baroreflex maintains cardiovascular stability by modulating heart rate, myocardial contraction, and vascular tone. However, noninvasive assessment of its sympathetic vascular and myocardial branches often overlooks their time-dependent interplay. To address this gap, we developed and implemented a noninvasive method that characterizes these baroreflex dynamics to enhance understanding of autonomic function and improve clinical assessments of cardiovascular regulation.<i>Approach.</i>We analyzed blood pressure and ECG recordings from 55 preoperative patients and 21 participants from the EUROBAVAR dataset. Baroreflex sensitivity (BRS) was calculated using the sequence method for interbeat interval (IBI), myocardial contractility (d<i>P</i>/d<i>t</i>_max), and systemic vascular resistance (SVR), derived through pulse contour analysis at multiple delays relative to beat-to-beat changes in systolic arterial pressure (SAP). Correlations of these BRS estimates with hemodynamic parameters and heart rate variability (HRV) were evaluated at rest and during active standing.<i>Main results.</i>Distinct temporal profiles of BRS for IBI, SVR, and d<i>P</i>/d<i>t</i>_maxwere identified, with significant correlations to HRV and average SVR, CO, and SAP levels at physiologically relevant delays. Orthostatic stress primarily impacted parasympathetic BRS for IBI, while BRS for SVR and d<i>P</i>/d<i>t</i>_maxshowed subtler changes, reflecting unique time-dependent associations.<i>Significance.</i>This approach provides a tool to comprehensively understand the baroreflex function, highlighting the latency-dependent interactions of its branches with their effectors and their adaptability to physiological challenges. Such insights could improve clinical assessments of autonomic dysfunction with altered baroreflex latencies and inform personalized strategies for managing conditions that compromise cardiovascular stability.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12042762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657430","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":"Inferring forced expiratory volume in 1 second (FEV1) from mobile ECG signals collected during quiet breathing.","authors":"Maria T Nyamukuru, Alix Ashare, Kofi M Odame","doi":"10.1088/1361-6579/adbaaf","DOIUrl":"10.1088/1361-6579/adbaaf","url":null,"abstract":"<p><p><i>Objective.</i>Forced expiratory volume in one second (FEV1) is an important metric for patients to track at home for their self-management of asthma and chronic obstructive pulmonary disease (COPD). Unfortunately, the state-of-the-art for measuring FEV1 at home either depends on the patient's physical effort and motivation, or relies on bulky wearable devices that are impractical for long-term monitoring. This paper explores the feasibility of using a machine learning model to infer FEV1 from 270 seconds of a single-lead electrocardiogram (ECG) signal measured on the fingers with a mobile device.<i>Methods.</i>We evaluated the model's inferred FEV1 values against the ground truth of hospital-grade spirometry tests, which were performed by twenty-five patients with obstructive respiratory disease.<i>Results.</i>The model-inferred FEV1 compared to the spirometry-measured FEV1 with a correlation coefficient of<i>r</i> = 0.73, a mean absolute percentage error of 23% and a bias of -0.08.<i>Conclusions.</i>These results suggest that the ECG signal contains useful information about FEV1, although a larger, richer dataset might be necessary to train a machine learning model that can extract this information with better accuracy.<i>Significance.</i>The benefit of a mobile ECG-based solution for measuring FEV1 is that it would require minimal effort, thus encouraging patient adherence and promoting successful self-management of asthma and COPD.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516330","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":"Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation.","authors":"Mads C F Hostrup, Anne Sofie Nielsen, Freja E Sørensen, Jesper O Kragballe, Morten U Østergaard, Emil Korsgaard, Samuel E Schmidt, Dan S Karbing","doi":"10.1088/1361-6579/adbe23","DOIUrl":"10.1088/1361-6579/adbe23","url":null,"abstract":"<p><p><i>Objective.</i>Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range.<i>Approach.</i>Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 s. A flow meter was used as reference. Furthermore, the PCA-autocorrelation method was compared with a similar single axis method.<i>Main results.</i>The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [-1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCA-autocorrelation method were within ±2 bpm of the reference. A Pearson correlation indicated a very strong correlation with<i>r</i> = 0.99 (p<0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [-14.9; 22.3 bpm], and<i>r</i> = 0.44 (p<0.001).<i>Significance.</i>The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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