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http://dx.doi.org/10.6109/jkiice.2013.17.3.705

Efficient QRS Detection and PVC(Premature Ventricular Contraction) Classification based on Profiling Method  

Cho, Ik-Sung (부산대학교 IT응용공학과)
Kwon, Hyeog-Soong (부산대학교 IT응용공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.17, no.3, 2013 , pp. 705-711 More about this Journal
Abstract
QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.
Keywords
morphological filter; QRS; normal signal; hash function; profiling; PVC;
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Times Cited By KSCI : 3  (Citation Analysis)
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