ECG Arrhythmia Classification Based on Wavelet Packet Transform and Sparse Non-Negative Matrix Factorization

Classification of ECG arrhythmia along with medical knowledge can lead to proper decision-making on the patientchr('39')s condition. Also, classification of arrhythmia types is one of the challenging issues due to the need for detailed analysis of the extracted feature from ECG signal. Therefore, addressing this field using signal processing techniques can be very important. In this paper, various types of morphological features are used to determine the type of ECG arrhythmia. Sparse structured principal component analysis and sparse non-negative matrix factorization algorithms are used to learn the over-complete models based on the characteristics of each data category. Also, the wavelet packet transform coefficients are calculated in different decomposition level to learn over-complete dictionaries. The results of this categorization are compared with the results of the classification based on the neural network, support vector machine another methods presented in this processing field. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of ECG arrhythmia precisely.