An Advanced Method Fusion and an Empirical Mode Decomposition-Based Filtering Methods for Breathing Rate Estimation from Seismocardiogram Signals

Abstract

Breathing Rate (BR), an important deterioration indicator, has been widely neglected in hospitals due to the requirement of invasive procedures and the need for skilled nurses to be measured. On the other hand, biomedical signals such as Seismocardiography (SCG), which measures heart vibrations transmitted to the chest-wall, can be used as a non-invasive technique to estimate the BR. This makes SCG signals a highly appealing way for estimating the BR. As such, this work proposes three novel methods for extracting the BR from SCG signals. The first method is based on extracting respiration-dependent features such as the fundamental heart sound components, S1 and S2 from the SCG signal. The second novel method investigates for the first time the use of data driven methods such as the Empirical Mode Decomposition (EMD) method to identify the respiratory component from an SCG signal. Finally, the third advanced method is based on fusing frequency information from the respiration signals that result from the aforementioned proposed methods and other standard methods. The developed methods in this paper are then evaluated on adult recordings from the combined measurement of ECG, the Breathing and Seismocardiograms database. Both fusion and EMD filter-based methods outperformed the individual methods, giving a mean absolute error of 1.5 breaths per minute, using a one-minute window of data.

Publication DOI: https://doi.org/10.3390/info12090368
Divisions: College of Engineering & Physical Sciences > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Funding: This research was funded by Isansys Lifecare Ltd. Grant No. NA.
Uncontrolled Keywords: breathing rate estimation,non-invasive monitoring,seismocardiogram,empirical mode decomposition,fusion methods,frequency domain analysis,autoregressive analysis
Full Text Link:
Related URLs: https://www.mdp ... 8-2489/12/9/368 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-11
Accepted Date: 2021-09-10
Authors: Kozia, Christina
Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)

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