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Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.
Sci Rep
January 2025
Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (emeritus), Yonezawa, Japan.
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidirectional long short-term memory network to train the model to minimize the loss function of the mean squared error between the predicted ECG (pECG) and genuine ECG signals. Using a dataset acquired with polyvinylidene fluoride and ECG sensors in different recumbent positions from 18 participants, we generated pECG signals from preprocessed BCG signals using the learned model and evaluated the RRI estimation performance by comparing the predicted RRI with the reference RRI obtained from the ECG signal using a leave-one-subject-out cross-validation scheme.
View Article and Find Full Text PDFUsing a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System.
Sensors (Basel)
December 2024
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan.
Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users.
View Article and Find Full Text PDF[Validation of ballistocardiography data for predicting the sleep efficiency in healthy individuals and patients with insomnia].
Zh Nevrol Psikhiatr Im S S Korsakova
November 2024
Samara State Medical University, Samara, Russia.
Objective: To identify valid parameters of heart rate variability (HRV) and musculoskeletal movements based on ballistocardiography data, which allow predicting the sleep efficiency (SE) index in healthy individuals and patients with insomnia.
Material And Methods: Ten healthy individuals and 14 patients with chronic insomnia were examined using polysomnography and ballistocardiography. A regression analysis of the data was carried out, as well as a reliability check of the resulting mathematical model.
A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments.
Sci Data
October 2024
Beijing Sport University, School of Sport Engineering, Beijing, 100084, China.
To facilitate unobtrusive and continuous sleep monitoring and promote intelligent sleep quality assessment, we present a dataset that includes multiple nights of continuous ballistocardiogram (BCG) data collected using piezoelectric film sensors from 32 subjects in their regular sleep environments. Besides, the referenced heart rate and respiratory data are also recorded by reference sensors to validate the accuracy of the cardiac and respiratory components extracted from the BCG signals. The dataset serves as a foundation for research on unobtrusive vital sign monitoring based on BCG signals, offering data support for the evaluation and optimization of sleep quality.
View Article and Find Full Text PDFEmerging Paradigms in Fetal Heart Rate Monitoring: Evaluating the Efficacy and Application of Innovative Textile-Based Wearables.
Sensors (Basel)
September 2024
Department of Materials, The University of Manchester, Manchester M13 9PL, UK.
This comprehensive review offers a thorough examination of fetal heart rate (fHR) monitoring methods, which are an essential component of prenatal care for assessing fetal health and identifying possible problems early on. It examines the clinical uses, accuracy, and limitations of both modern and traditional monitoring techniques, such as electrocardiography (ECG), ballistocardiography (BCG), phonocardiography (PCG), and cardiotocography (CTG), in a variety of obstetric scenarios. A particular focus is on the most recent developments in textile-based wearables for fHR monitoring.
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