Heartbeat Detection in Seismocardiograms with Semantic Segmentation

Konrad M. Duraj, Szymon Siecinski*, Rafal J. Doniec, Natalia J. Piaseczna, Pawel S. Kostka, Ewaryst Janusz Tkacz

*Corresponding author for this work

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 11-15 July 2022, Glasgow, Scotland, UK
Publication date08.09.2022
Pages662-665
ISBN (Electronic)978-1-7281-2782-8
DOIs
Publication statusPublished - 08.09.2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Scottish Event Campus (SEC) Centre, Glasgow, United Kingdom
Duration: 11.07.202215.07.2022
https://embc.embs.org/2022

Cite this