A 1D convolution neural network model achieved a classification accuracy of 97.5% for 5 typical kinds of arrhythmia signals on the MIT-BIH database, outperforming typical methods.
Does a 1D convolution neural network improve the classification accuracy of ECG arrhythmia signals compared to typical classification methods?
A 1D convolutional neural network can automatically and accurately classify five typical types of arrhythmias from ECG signals with 97.5% accuracy.
Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features automatically. This model realizes the classification of 5 typical kinds of arrhythmia signals, i.e., normal, left bundle branch block, right bundle branch block, atrial premature contraction and ventricular premature contraction. The experimental results on the public MIT-BIH arrhythmia database show that the proposed method achieves a promising classification accuracy of 97.5%, significantly outperforming several typical ECG classification methods.
Li et al. (2017) studied Arrhythmia. 1D convolution neural network (CNN) vs. Typical ECG classification methods was evaluated on Classification accuracy of 5 typical kinds of arrhythmia signals. A 1D convolution neural network model achieved a classification accuracy of 97.5% for 5 typical kinds of arrhythmia signals on the MIT-BIH database, outperforming typical methods.