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

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IJCA Proceedings on International Conference on Computing, Communication and Sensor Network
© 2015 by IJCA Journal
CCSN 2014 - Number 1
Year of Publication: 2015
Authors:
Narottam Das
Alok Chakrabarty

Narottam Das and Alok Chakrabarty. Article: Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features. IJCA Proceedings on International Conference on Computing, Communication and Sensor Network CCSN 2014(1):16-20, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Narottam Das and Alok Chakrabarty},
	title = {Article: Arrhythmia Classification by Heart Rate Variability Analysis using Symlets based on Time-Frequency Features},
	journal = {IJCA Proceedings on International Conference on Computing, Communication and Sensor Network},
	year = {2015},
	volume = {CCSN 2014},
	number = {1},
	pages = {16-20},
	month = {June},
	note = {Full text available}
}

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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