Key result
The CNN-FWS deep learning model diagnosed normal and abnormal 12-lead ECG signals with an F1 score of 0.902 and a Recall of 0.889.
Why the study?
ECG is crucial for early detection and prevention of cardiovascular disease, motivating an improved deep learning method to accurately diagnose normal and abnormal ECG signals.
Does the CNN-FWS deep learning model accurately diagnose normal and abnormal 12-lead ECG signals?
Does the CNN-FWS deep learning model accurately diagnose normal and abnormal 12-lead ECG signals?
Effect estimate: F1 score 0.902, Recall 0.889
The proposed CNN-FWS deep learning model demonstrates high diagnostic accuracy for distinguishing normal and abnormal 12-lead ECG signals, enabling an automated, end-to-end adaptive feature selection process.
May support hybrid CNN development for ECG classification; leaves open prospective clinical validation before practice change.
(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today's world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.
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Zhu et al. (2022) studied Normal and abnormal ECG (n=17,259). CNN-FWS deep learning model was evaluated on Diagnostic performance (F1 score and Recall) (F1 score 0.902, Recall 0.889). The CNN-FWS deep learning model diagnosed normal and abnormal 12-lead ECG signals with an F1 score of 0.902 and a Recall of 0.889.
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