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Reseach Article

Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features

Published on June 2015 by Narottam Das, Alok Chakrabarty
International Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN2014 - Number 1
June 2015
Authors: Narottam Das, Alok Chakrabarty
b18bc8ab-b680-4111-8083-5a9c436459c0

Narottam Das, Alok Chakrabarty . Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features. International Conference on Computing, Communication and Sensor Network. CCSN2014, 1 (June 2015), 16-20.

@article{
author = { Narottam Das, Alok Chakrabarty },
title = { Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features },
journal = { International Conference on Computing, Communication and Sensor Network },
issue_date = { June 2015 },
volume = { CCSN2014 },
number = { 1 },
month = { June },
year = { 2015 },
issn = 0975-8887,
pages = { 16-20 },
numpages = 5,
url = { /proceedings/ccsn2014/number1/21418-5013/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing, Communication and Sensor Network
%A Narottam Das
%A Alok Chakrabarty
%T Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features
%J International Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN2014
%N 1
%P 16-20
%D 2015
%I International Journal of Computer Applications
Abstract

The activities of autonomic nervous system can be accessed using information on heart rate modulation mechanism. HRV analysis is a well-known non-invasive tool that gives information on heart rate modulation mechanism. This paper presents a work on HRV analysis to distinguish normal sinus rhythm from atrial fibrillation, supra-ventricular arrhythmia and premature ventricular contraction. Basically a technique for detection of the heart disease Arrhythmia grounding on HRV signal data analysis is presented in this paper. The R-Peak detection is done using wavelet Symlet7 at second level decomposition. The time-frequency parameters such as SD Ratio, LF/HF Ratio and pNN50 are used for HRV analysis. The ratio LF/HF of HRV spectra represents a measure of sympatho-vagal balance. As this parameter shows better results for only short term recordings hence other parameters such as SD Ratio and pNN50 are considered for HRV analysis for both long-term and short-term recordings.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Heart Rate Variability Symlet Sd Ratio Lf/hf Pnn50 Poincaré Plot Psd Plot Heart Rate Heart Beat Hrv Wavelet