Deep Learning Modeling of Cardiac Arrhythmia Classification on Information Feature Fusion Image with Attention Mechanism

Abstract

The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in this paper. The approach improves the feature extraction capability and classification accuracy via techniques of image conversion and attention mechanism. In terms of the recognition strategy, this paper presents an image conversion using relative position matrix information. This information is utilized to describe the relative spatial relationships between different waveforms, and the image identification is successfully applied to the Gam-Resnet18 deep learning network model with a transfer learning concept for classification. Ultimately, this model achieved a total accuracy of 99.30%, an average positive prediction rate of 98.76%, a sensitivity of 98.90%, and a specificity of 99.84% with the relative position matrix approach. To evaluate the effectiveness of the proposed method, different image conversion techniques are compared on the test set. The experimental results demonstrate that the relative position matrix information can better reflect the differences between various types of arrhythmias, thereby improving the accuracy and stability of classification.

Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

“…This stage focuses on preparing the ECG data for subsequent analysis and model training. A common preprocessing step, employed by both CML and DL approaches, involves denoising the ECG signal to remove unwanted artifacts [45,[47][48][49]. Denoising aims to mitigate or eliminate the distorting influence of artifacts, which can originate from diverse sources such as respiration, body movements, electrode contact issues, and skinelectrode impedance.…”