A Computationally Light-Weight Real-Time Classification Method To Identify Different ECG Signals
Ventricular arrhythmia is the main cause of cardiac arrest in patients with chronic heart disease. An undetected episode of ventricular tachycardia (VT) can be fatal if emergency medical assistance is not provided. Therefore, it is important to devise a real-time mobile ECG signal analysis algorithm for detection of ventricular tachycardia (VT). This paper presents an algorithm for automatic identification of normal sinus rhythm (NSR) and ventricular tachycardia (VT) which is applicable in a mobile environment. The algorithm employs peak-valley detector and crosscorrelation technique to compute a feature vector. The selected features are beats-per-minute (BPM), NSR template accuracy and VT template accuracy. Based on the selected features, a fuzzy k-NN classifier is trained for classification. The algorithm specificity and sensitivity for classifying between NSR and VT ECG signal is 92.5％ and 93.5％ respectively.
Fook Joo Chin Qiang Fang Irena Cosic
School of Electrical &Computer Engineering, RMIT University, Melbourne, Australia
国际会议
2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)
苏州
英文
287-290
2011-11-03（万方平台首次上网日期，不代表论文的发表时间）