Electrocardiogram (ECG) is a physiological signal widely used in monitoring heart health, which is of great significance to the detection and diagnosis of heart diseases. Because abnormal heart rhythms are very rare, most ECG datasets have data imbalance problems. At present, many algorithms for ECG anomaly automatic recognition are affected by data imbalance. Conventional data augmentation methods are not suitable for the augmentation of the ECG signal, because the ECG signal is one-dimensional and their morphology has physiological significances. In this paper, we propose a ProGAN based ECG sample generation model, called ProEGAN-MS, to solve the problem of data imbalance. The model can stably generate realistic ECG samples. We evaluate the fidelity and diversity of the data generated by the model and compare the data distribution of the original and generated data. In addition, in order to show the diversity of the generated ECG data more intuitively, we manually checked the diversity and calculate the statistics of the data. The results show that compared with other ECG augmentation methods based on GANs, the ECG data generated by our model has higher fidelity and diversity, and the distribution of generated samples is closer to the distribution of original data. Finally, we established neural network models for arrhythmia classification, and used them to evaluate the improvement of the classification model performance by ProEGAN-MS. The results show that augmented data by ProEGAN-MS can effectively improve the insufficient sensitivity and precision of the classification model.

Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.