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Reduced lead ECG multi-label classification with higher generalization using 2D SEResnets with self attention

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Abstract

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, and early detection is crucial for effective treatment and management. Electrocardiogram (ECG) signals have been widely used for CVD diagnosis due to their non-invasiveness and ease of acquisition. However, the complexity of ECG signals and the presence of multiple CVDs in a single patient make the classification task challenging. Therefore, the early detection and diagnosis of cardiovascular diseases (CVDs) using electrocardiogram (ECG) signals is critical to preventing associated complications. This article proposes a novel approach for the reduced lead ECG multi-label classification task using 2D SEResnet with an attention mechanism. The proposed approach employs a 2D CNN architecture that incorporates wide SE residual networks (SEResnet) and a self-attention mechanism to extract relevant features from ECG signals with fewer leads. By evaluating a dataset with over 88,000 ECG records, the model showed high accuracy and an F1 score for identifying various abnormalities. Also, the model includes certain demographic features, which further improve the generality of the model. Overall, the model achieved an accuracy of 42.9% and an F1-score of 0.51 for a single-lead configuration and 48% and an F1-score of 0.55 for a two-lead configuration. These results indicate that the proposed approach can effectively perform with limited ECG leads and some demographic data, which could be helpful in real-world scenarios.

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Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India

    Danish Sheikh & Naveen Chauhan

  2. Amity School of Engineering & Technology, Amity University Punjab, Mohali, Punjab, 140306, India

    Himanshu Verma

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  1. Danish Sheikh
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  2. Himanshu Verma
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  3. Naveen Chauhan
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Contributions

Danish Sheikh: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Writing: original draft, Writing: review & editing, Visualization, Validation, Software. Himanshu Verma: Supervision, Conceptualization, Formal analysis, Investigation, Visualization, Validation. Naveen Chauhan: Supervision, Investigation, Visualization, Validation.

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Correspondence to Himanshu Verma.

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Sheikh, D., Verma, H. & Chauhan, N. Reduced lead ECG multi-label classification with higher generalization using 2D SEResnets with self attention. Multimed Tools Appl 83, 65315–65339 (2024). https://doi.org/10.1007/s11042-024-18116-z

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