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<p>Physical activity is very strongly associated with cardiovascular as well as overall health. A plethora of research has evidenced a decline in all-cause mortality rates and a diminished prevalence of cardiovascular ailments, malignancies, and metabolic disorders among individuals whose lifestyle had regular physical exercise. In the context of these beneficial outcomes, the phenomenon of sudden cardiac death (SCD) continues to present a distressing reality, even in seemingly fit athletes competing at elite standards. Sudden Cardiac Death (SCD) is characterized as an unforeseen demise attributable to cardiac causes, and currently, there exists no established medical protocols capable of averting its occurrence. The most effective preventative measure is to consistently monitor vital signs and detect irregularities to facilitate prompt intervention. In this study, a new Adaptive Multiscale Dilated Bidirectional Long Short-Term Memory (Bi-LSTM) network for ECG Anomaly Classification using PTB-XL dataset (PhysioNet 2022) has been developed. This network leverages multiscale dilated convolution to efficiently capture long-range dependencies while preserving fine-grained temporal features. The proposed model demonstrated robust learning capabilities, achieving a training accuracy of 90% and stabilizing at a validation accuracy of 88% through the implementation of mixed precision training and early stopping at 40 epochs. Evaluation metrics highlight the model’s reliability for clinical screening, evidenced by a high specificity of 92.91% and a precision of 77.27% (F1 score: 0.7379), effectively minimizing false positives. However, discrepancies in AUC scores (0.67 for Normal vs. 0.57 for Pathological) and a moderate sensitivity of 70.62% indicate challenges in distinguishing subtle abnormalities, suggesting that future iterations could benefit from targeted data augmentation to address class imbalances.</p>

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Adaptive multiscale dilated Bi-LSTM for automated ECG anomaly detection and classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月1日

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  • V. Neelanaryanan,
  • S. K. Sudarsanam,
  • V. Prakash,
  • C. Naveen,
  • M. Sivakumar

摘要

Physical activity is very strongly associated with cardiovascular as well as overall health. A plethora of research has evidenced a decline in all-cause mortality rates and a diminished prevalence of cardiovascular ailments, malignancies, and metabolic disorders among individuals whose lifestyle had regular physical exercise. In the context of these beneficial outcomes, the phenomenon of sudden cardiac death (SCD) continues to present a distressing reality, even in seemingly fit athletes competing at elite standards. Sudden Cardiac Death (SCD) is characterized as an unforeseen demise attributable to cardiac causes, and currently, there exists no established medical protocols capable of averting its occurrence. The most effective preventative measure is to consistently monitor vital signs and detect irregularities to facilitate prompt intervention. In this study, a new Adaptive Multiscale Dilated Bidirectional Long Short-Term Memory (Bi-LSTM) network for ECG Anomaly Classification using PTB-XL dataset (PhysioNet 2022) has been developed. This network leverages multiscale dilated convolution to efficiently capture long-range dependencies while preserving fine-grained temporal features. The proposed model demonstrated robust learning capabilities, achieving a training accuracy of 90% and stabilizing at a validation accuracy of 88% through the implementation of mixed precision training and early stopping at 40 epochs. Evaluation metrics highlight the model’s reliability for clinical screening, evidenced by a high specificity of 92.91% and a precision of 77.27% (F1 score: 0.7379), effectively minimizing false positives. However, discrepancies in AUC scores (0.67 for Normal vs. 0.57 for Pathological) and a moderate sensitivity of 70.62% indicate challenges in distinguishing subtle abnormalities, suggesting that future iterations could benefit from targeted data augmentation to address class imbalances.