关键词: 心律失常分类, 循环谱, 超限学习机

Abstract: The performance of ECG arrhythmia classification mainly depends on both the effective feature extraction and the optimal design of the classifier. Most of the classic methods extract the time domain features or frequency domain features directly to achieve the arrhythmia classification, but the classification performance still needs to be improved for multi-classification tasks. For this issue, the cyclic spectrum analysis technique is used to achieve the multi-arrhythmia classification. The method assumes that the signal is in non-stationary state, and arrhythmia classification can be implemented through establishing a model to capture the hidden period in the ECG signal, which is more appropriate with the actual state of ECG signals. In order to implement the arrhythmia classification, the morphological features and wavelet coefficients time-frequency domain features are extracted. In addition, the cyclic spectrum technology is adopted for extracting the spectral correlation features for the subsequent multi-classification task. Besides, a comparison on the classification performance is also conducted among the artificial neural networks, the traditional support vector machine classifier and extreme learning machine. Experimental results show that the proposed method based on the extreme learning machine can classify ten types of arrhythmias and achieve an average classification accuracy of 98.13% on the MIT-BIH arrhythmia benchmark dataset.

Key words: arrhythmia classification, cyclic spectral, extreme learning machine