A Fast ECG Diagnosis by Using Non-Uniform Spectral Analysis and the Artificial Neural Network

The electrocardiogram (ECG) has been proven as an efficient diagnostic tool to monitor the electrical activity of the heart and has become a widely used clinical approach to diagnose heart diseases. In a practical way, the ECG signal can be decomposed into P, Q, R, S, and T waves. Based on the information of the features in these waves, such as the amplitude and the interval between each wave, many types of heart diseases can be detected by using the neural network (NN)-based ECG analysis approach. However, because of a large amount of computing to preprocess the raw ECG signal, it is time consuming to analyze the ECG signal in the time domain. In addition, the non-linear ECG signal analysis worsens the difficulty to diagnose the ECG signal. To solve the problem, we propose a fast ECG diagnosis approach based on spectral analysis and the artificial neural network. Compared with the conventional time-domain approaches, the proposed approach analyzes the ECG signal only in the frequency domain. However, because most of the noises in the raw ECG signal belong to high-frequency signals, it is necessary to acquire more features in the low-frequency spectrum and fewer features in the high-frequency spectrum. Hence, a non-uniform feature extraction approach is proposed in this article. According to less data preprocessing in the frequency domain than the one in the time domain, the proposed approach not only reduces the total diagnosis latency but also reduces the computing power consumption of the ECG diagnosis. To verify the proposed approach, the well-known MIT-BIH arrhythmia database is involved in this work. The experimental results show that the proposed approach can reduce ECG diagnosis latency by 47% to 52% compared with conventional ECG analysis methods under similar diagnostic accuracy of heart diseases. In addition, because of less data preprocessing, the proposed approach can achieve lower area overhead by 22% to 29% and lower computing power consumption by 29% to 34% compared with the related works, which is proper for applying this approach to portable medical devices.