Arrhythmias Classification Using Short-Time Fourier Transform and GAN Based Data Augmentation

Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrhythmias classification models with high accuracy using artificial neural networks. To solve this problem, we propose a novel data augmentation method based on short-time Fourier transform (STFT) and generative adversarial network (GAN) to obtain evenly distributed samples in the training dataset. Firstly, the one-dimensional electrocardiogram (ECG) signals with a fixed length of 6 s are subjected to STFT to obtain the coefficient matrices, and then the matrices of different heart rhythm samples are used to train GAN models respectively. The generated matrices are later employed to augment the training dataset of classification models based on four convolutional neural networks (CNNs). The result shows that the performances of the classification networks are all improved after we adopt the data enhancement strategy. The proposed method is helpful in augmentation and classification of biomedical signals, especially in detecting multiple arrhythmias, since adequate training samples are usually inaccessible in these studies.