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Comparative study of applying signal processing techniques on ballistocardiogram in detecting J-Peak using Bi-LSTM Model

  • Cardiovascular diseases (CVD) are leading contributors to global mortality, necessitating advanced methods for vital sign monitoring. Heart Rate Variability (HRV) and Respiratory Rate, key indicators of cardiovascular health, are traditionally monitored via Electrocardiogram (ECG). However, ECG's obtrusiveness limits its practicality, prompting the exploration of Ballistocardiography (BCG) as a non-invasive alternative. BCG records the mechanical activity of the body with each heartbeat, offering a contactless method for HRV monitoring. Despite its benefits, BCG signals are susceptible to external interference and present a challenge in accurately detecting J-Peaks. This research uses advanced signal processing and deep learning techniques to overcome these limitations. Our approach integrates accelerometers for long-term BCG data collection during sleep, applying Discrete Wavelet Transforms (DWT) and Ensemble Empirical Mode Decomposition (EEMD) for feature extraction. The Bi-LSTM model, leveraging these features, enhances heartbeat detection, offering improved reliability over traditional methods. The study's findings indicate that the combined use of DWT, EEMD, and Bi-LSTM for J-Peak detection in BCG signals is effective, with potential applications in unobtrusive long-term cardiovascular monitoring. Our results suggest that this methodology could contribute to HRV monitoring, particularly in home settings, enhancing patient comfort and compliance.

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Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad
URL:http://www.dii.univpm.it/MAES-2023
ISBN:978-88-87548-00-6
Erschienen in:Models and applications for embedded systems
Publisher:Università Politecnica delle Marche
Place of publication:Ancona
Editor:Massimo Conti, Simone Orcioni
Document Type:Book chapter
Language:English
Publication year:2024
Contributing Corporation:Carl Zeiss Foundation
Page Number:8
First Page:23
Last Page:30
DDC classes:610 Medizin, Gesundheit
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt