Improved deep convolutional neural network GoogLeNet for human ECG signal classification

Abstract

Electrocardiogram (ECG) is the most commonly used diagnostic method for heart disease. However, in the process of ECG signal acquisition, it is often interfered by noise, which greatly affects the accuracy of ECG signal classification and diagnosis. To improve the preciseness of ECG classification and diagnosis, we propose a method using continuous wavelet transform (CWT) combined with a deep convolutional network GoogLeNet. We used three sets of ECG data from the PhysioNet database for cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythms (NSR). First, we construct time-frequency representations of ECG signals using CWT, which are known as scale maps. Second, we improve GoogLeNet to better recognize ECG images, which has an accuracy of 96.87%. Finally, this paper visualizes the network filter weights from the first convolutional layer and finds the most relevant channel for the original input, and compares the most relevant channel with the original input to analyze the interpretability of deep neural networks.