Heartbeat Detection in Seismocardiograms with Semantic Segmentation
العنوان: | Heartbeat Detection in Seismocardiograms with Semantic Segmentation |
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المؤلفون: | Konrad M. Duraj, Szymon Siecinski, Rafal J. Doniec, Natalia J. Piaseczna, Pawel S. Kostka, Ewaryst J. Tkacz |
المصدر: | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2022 |
سنة النشر: | 2022 |
مصطلحات موضوعية: | Electrocardiography, Heart Rate, Reproducibility of Results, Neural Networks, Computer, Semantics |
الوصف: | Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability. |
تدمد: | 2694-0604 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81f3867ed80a5490705d676ee4fffa64 https://pubmed.ncbi.nlm.nih.gov/36086330 |
حقوق: | CLOSED |
رقم الأكسشن: | edsair.doi.dedup.....81f3867ed80a5490705d676ee4fffa64 |
قاعدة البيانات: | OpenAIRE |
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