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Mathematics
Volume 13
Issue 23
10.3390/math13233882
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion

by
Kan Luo
1,2,*,
Longying Huang
1,2,
Haixin He
2,
Yu Chen
1,2,
Lu You
1,2,
Siluo Chen
1,2,
Jian Chen
1 and
Chengyu Liu
3,*
1
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
2
Fuzhou Industrial Integration Automation Technology Innovation Center, Fuzhou 350118, China
3
State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
*
Authors to whom correspondence should be addressed.
Mathematics 2025, 13(23), 3882; https://doi.org/10.3390/math13233882 (registering DOI)
Submission received: 3 October 2025 / Revised: 19 November 2025 / Accepted: 2 December 2025 / Published: 3 December 2025
(This article belongs to the Special Issue Advanced Artificial Intelligence Models and Its Applications, 2nd Edition)
Versions Notes

Abstract

Deep learning has advanced automated electrocardiogram (ECG) interpretation, yet many models are computationally expensive, opaque, and overlook demographic factors. We propose DBA-ASFNet, a lightweight network that combines depthwise-separable convolutional residual blocks with a BiGRU and an attention mechanism to extract rich spatiotemporal features from 12-lead ECGs while maintaining low computational requirements. The Age-and-Sex Fusion (ASF) module integrates demographic information without enlarging the model, enabling personalized predictions. On the PTB-XL and CPSC2018 datasets, DBA-ASFNet achieves competitive multi-label performance with only ~0.03 million parameters and ~6.43 MFLOPs per inference. Real-time testing on a Raspberry Pi 5 achieved an average inference latency of ~2 ms, supporting deployment on resource-limited devices. Shapley additive explanations (SHAP) analysis shows that the model focuses on clinically meaningful ECG patterns and appropriately incorporates demographic factors, enhancing transparency. These results suggest that DBA-ASFNet is suited for accurate, efficient, and interpretable ECG analysis.
Keywords: electrocardiogram; lightweight model; DSCR; BiGRU; attention mechanism; demographic features; SHAP; interpretability electrocardiogram; lightweight model; DSCR; BiGRU; attention mechanism; demographic features; SHAP; interpretability

Share and Cite

MDPI and ACS Style

Luo, K.; Huang, L.; He, H.; Chen, Y.; You, L.; Chen, S.; Chen, J.; Liu, C. Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion. Mathematics 2025, 13, 3882. https://doi.org/10.3390/math13233882

AMA Style

Luo K, Huang L, He H, Chen Y, You L, Chen S, Chen J, Liu C. Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion. Mathematics. 2025; 13(23):3882. https://doi.org/10.3390/math13233882

Chicago/Turabian Style

Luo, Kan, Longying Huang, Haixin He, Yu Chen, Lu You, Siluo Chen, Jian Chen, and Chengyu Liu. 2025. "Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion" Mathematics 13, no. 23: 3882. https://doi.org/10.3390/math13233882

APA Style

Luo, K., Huang, L., He, H., Chen, Y., You, L., Chen, S., Chen, J., & Liu, C. (2025). Efficient and Interpretable ECG Abnormality Detection via a Lightweight DSCR-BiGRU-Attention Network with Demographic Fusion. Mathematics, 13(23), 3882. https://doi.org/10.3390/math13233882

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