ECG Arrhythmia Classification Using a Convolution Neural Network
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In this research paper, we propose a method for electrocardiogram (ECG) arrhythmia classification using a convolutional neural network (CNN). CNN has established to have a great performance in ECG classification and pattern recognition. The proposed CNN architecture in here classifies the electrocardiogram (ECG) arrhythmia into four distinct categories: normal sinus rhythm, atrial fibrillation, other rhythms, or too noisy to be classified. The ECG signal is converted into a two-dimensional spectrogram image as an input data for the CNN classifier. The proposed architecture includes various deep learning techniques such as batch normalization, data augmentation, and averaging-based feature aggregation across time. We use resampling and dropout bursts techniques for data augmentation. Our experimental results have successfully proven that the proposed CNN classifier with the augmented ECG data can achieve a classification accuracy of over 86.7%.
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