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The Progress of Processing, Featuring and Classification for ECG Signal

Cardiovascular disease is one of the several major diseases that threaten human health. Thus, it is important to analyze and diagnose related diseases based on electrocardiogram (ECG) signal identification and classification. In this paper, an overview of preprocessing, feature extraction and classification of ECG signals were presented. Specifically, this paper briefly analyzed three aspects involving signal pre-processing, feature extraction and feature classification. In the area of signal pre-processing and feature extraction, common methods were briefly described and algorithms from the last three years were described. In the area of feature classification, the relevant literature in the past three years was reviewed with discussing the algorithms through different network architectures in terms of performance metrics (e.g., accuracy, specificity and sensitivity). Existing deep learning algorithms were roughly classified into three main categories, namely convolutional neural network, short-time memory and multimodal deep learning. Besides, the literature was categorized with their features and improvements described separately. Problems prevalent in the current research were also analyzed and future research was envisaged. These results provide a guide for future research on the accurate identification and classification of ECG signals.