A novel algorithm for HRV estimation from short-term acoustic recordings at neck
File(s)
Accepted version
Author(s)
Sharma, Piyush
Rodriguez-Villegas, Esther
Type
Conference Paper
Abstract
Heart rate variability (HRV) is an important non-invasive parameter to monitor the activity of the autonomicnervous system. This paper proposes an algorithm to analyzeHRV by processing the acoustic data, recorded by placing asmall, wearable sensor on the suprasternal notch (at neck) ofan adult subject, primarily intended to record breathing sounds.The method used an empirical data analysis approach of theHilbert-Huang transform (HHT) to construct an instantaneousenergy envelope and segment the cardiac cycle by detectingS1 and S2 sounds using the K-means algorithm. The time-domain HRV analysis for the short-term recordings of 10subjects demonstrated a close agreement with the referenceECG signal. The instantaneous heart rate (IHR) comparisonsyielded an accuracy of 95.78% and 92.35% for S1 and S2sounds respectively. The experimental results showed that theproposed algorithm can provide an accurate HRV analysis forthe cardiac signals recorded at the neck.
Date Issued
2019-10-07
Date Acceptance
2019-04-12
Citation
2019
Publisher
IEEE
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Identifier
https://ieeexplore.ieee.org/abstract/document/8857525
Grant Number
724334
Source
41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publication Status
Published
Start Date
2019-07-23
Finish Date
2019-07-27
Coverage Spatial
Berlin, Germany
Date Publish Online
2019-10-07