ArticleCardiovascular engineering and technology2024
S4D-ECG: A Shallow State-of-the-Art Model for Cardiac Abnormality Classification.
Article in Cardiovascular engineering and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hearables: Bioelectronics technological challenges and opportunities.Wearable technologies · 2025Review
- Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.PNAS nexus · 2024Review
- Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.Journal of cardiovascular translational research · 2024Article
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Authors and funding
6 authors.
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Abstract
purposeThis study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities.
methodsThe proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, striking a balance between computational efficiency and predictive performance. This comprehensive approach ensures the model's suitability for real-time processing on hardware devices with limited capabilities, offering a streamlined yet effective solution for heart monitoring.
resultsAmong the notable features of this algorithm is its strong resilience to noise, enabling the algorithm to achieve an average F1-score of 81.2% and an AUROC of 95.5% in generalization. The model underwent testing specifically on the lead II ECG signal, exhibiting consistent performance with an F1-score of 79.5% and an AUROC of 95.7%.
conclusionIt is characterized by the elimination of pre-processing features and the availability of a low-complexity architecture that makes it suitable for implementation on numerous computing devices because it is easily implementable. Consequently, this algorithm exhibits considerable potential for practical applications in analyzing real-world ECG data. This model can be placed on the cloud for diagnosis. The model was also tested on lead II of the ECG alone and has demonstrated promising results, supporting its potential for on-device application.
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