Improved deep convolutional neural network GoogLeNet for human ECG signal classification

Wenchao Ren; Wei Cao; Zeyuan Liu

doi:10.1117/12.2641821

10 November 2022 Improved deep convolutional neural network GoogLeNet for human ECG signal classification

Wenchao Ren, Wei Cao, Zeyuan Liu

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480R (2022) https://doi.org/10.1117/12.2641821
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

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.

Citation Download Citation

Wenchao Ren, Wei Cao, and Zeyuan Liu "Improved deep convolutional neural network GoogLeNet for human ECG signal classification", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480R (10 November 2022); https://doi.org/10.1117/12.2641821

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