The S4D-ECG model provides a low-complexity, noise-resilient algorithm for cardiac abnormality classification from raw 12-lead ECG data with high accuracy.
An algorithm for processing raw 12-lead ECG data has been developed and validated in this study that is based on the S4D model. Among 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%. It is characterized by the elimination of pre-processing features as well as 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.
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Huang et al. (2023) studied this question.
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