University of Oulu

Ferdinando, H.; Seppälä, E.; Myllylä, T. Discrete Wavelet Transforms-Based Analysis of Accelerometer Signals for Continuous Human Cardiac Monitoring. Appl. Sci. 2021, 11, 12072.

Discrete wavelet transforms-based analysis of accelerometer signals for continuous human cardiac monitoring

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Author: Ferdinando, Hany1,2; Seppälä, Eveliina1; Myllylä, Teemu1,3
Organizations: 1Research Unit of Medical Imaging, Physics, and Technology, Oulu University, 90100 Oulu, Finland
2Department of Electrical Engineering, Petra Christian University, Surabaya 60236, Indonesia
3Optoelectronics and Measurement Technique Research Unit, Oulu University, 90570 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.5 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2021
Publish Date: 2022-02-03


Measuring cardiac activity from the chest using an accelerometer is commonly referred to as seismocardiography. Unfortunately, it cannot provide clinically valid data because it is easily corrupted by motion artefacts. This paper proposes two methods to improve peak detection from noisy seismocardiography data. They rely on discrete wavelet transform analysis using either biorthogonal 3.9 or reverse biorthogonal 3.9. The first method involves slicing chest vibrations for each cardiac activity, and then detecting the peak location, whereas the other method aims at detecting the peak directly from chest vibrations without segmentation. Performance evaluations were conducted on signals recorded from small children and adults based on missing and additional peaks. Both algorithms showed a low error rate (15.4% and 2.1% for children/infants and adults, respectively) for signals obtained in resting state. The average error for sitting and breathing tasks (adults only) was 14.4%. In summary, the first algorithm proved more promising for further exploration.

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Series: Applied sciences
ISSN: 2076-3417
ISSN-E: 2076-3417
ISSN-L: 2076-3417
Volume: 11
Issue: 24
Article number: 12072
DOI: 10.3390/app112412072
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This research was funded by Academy of Finland, grant number 318347 and 335723.
Academy of Finland Grant Number: 318347
Detailed Information: 318347 (Academy of Finland Funding decision)
335723 (Academy of Finland Funding decision)
Copyright information: © 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 (