Abstract
Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.
Highlights
Cardiac arrhythmia is an essential manifestation of cardiovascular disease (CVD)
Automatic Detection of Arrhythmia (ADA) refers to the use of computer equipment to replace the tedious and time-consuming manual ECG data analysis, which will not lead to recognition problems due to expert differences or fatigue
A typical solution for ADA is, to [5], for each heartbeat, to combine the features extracted by wavelet transformation (WT) and independent component analysis (ICA) with the four RR interval features designed and transmit all of them to a support
Summary
Cardiac arrhythmia is an essential manifestation of cardiovascular disease (CVD). Electrocardiogram (ECG) is a comprehensive tool widely used by clinicians in hospitals which captures the propagation of electrical signals in the heart from the body surface and the state of the cardiovascular system can be detected through the morphology and rhythm of ECG. Automatic Detection of Arrhythmia (ADA) refers to the use of computer equipment to replace the tedious and time-consuming manual ECG data analysis, which will not lead to recognition problems due to expert differences or fatigue. The ADA problem has been widely researched in recent decades and the research can be generally divided into the following three steps: ECG data preprocessing, feature extraction, and classification, where data preprocessing includes noise filtering, baseline removal, and signal segmentation [4]. A typical solution for ADA is, to [5], for each heartbeat, to combine the features extracted by wavelet transformation (WT) and independent component analysis (ICA) with the four RR interval features designed and transmit all of them to a support
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