R Wave Detection Algorithm Based Adaptive Variable Threshold and Window for PVC Classification 


Vol. 34,  No. 11, pp. 1289-1295, Nov.  2009


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  Abstract

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

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  Cite this article

[IEEE Style]

I. Cho and H. Kwon, "R Wave Detection Algorithm Based Adaptive Variable Threshold and Window for PVC Classification," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 11, pp. 1289-1295, 2009. DOI: .

[ACM Style]

Ik-Sung Cho and Hyeog-Soong Kwon. 2009. R Wave Detection Algorithm Based Adaptive Variable Threshold and Window for PVC Classification. The Journal of Korean Institute of Communications and Information Sciences, 34, 11, (2009), 1289-1295. DOI: .

[KICS Style]

Ik-Sung Cho and Hyeog-Soong Kwon, "R Wave Detection Algorithm Based Adaptive Variable Threshold and Window for PVC Classification," The Journal of Korean Institute of Communications and Information Sciences, vol. 34, no. 11, pp. 1289-1295, 11. 2009.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880