• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network


Hyoung-Jong Jang, Joon-Shik Lim, Journal of Internet Computing and Services, Vol. 10, No. 5, pp. 107-116, Oct. 2009
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Keywords: Heart Rate Variability, Fuzzy Neural Network, Arrhythmia

Abstract

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.


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Cite this article
[APA Style]
Jang, H. & Lim, J. (2009). Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network. Journal of Internet Computing and Services, 10(5), 107-116.

[IEEE Style]
H. Jang and J. Lim, "Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network," Journal of Internet Computing and Services, vol. 10, no. 5, pp. 107-116, 2009.

[ACM Style]
Hyoung-Jong Jang and Joon-Shik Lim. 2009. Detection of Arrhythmia Using Heart Rate Variability and A Fuzzy Neural Network. Journal of Internet Computing and Services, 10, 5, (2009), 107-116.