Nowadays, elderly suffer from many chronic diseases. One of the most frequent chronic diseases is heart disease. Electrocardiogram (ECG) is a signal that carries important information for indicating the normal and abnormal status of cardiac patients. In the early stage, abnormal ECG may occur intermittently. The detection of heart diseases for prevention and treatment is therefore a crucial task. So, The arrhythmia detection can support
physicians for cardiac analysis and also increase performance in preventive health care. Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing
time. But it is difficult to detect the P and T wave signal
because of person’s individual difference. Detection algorithm shout be able to detect episodes of arrhythmia accurately and at the same time have a low computational complexity in order to analyze ECG signals in real time. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose arrhythmia detection based on
binary coding using QRS feature change rate. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold change rate of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The
achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.