Key result
An ensemble deep learning approach combining CNN and SLAP architecture achieved 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity for ECG classification on the MIT-BIH database.
Why the study?
Intelligent detection and diagnosis of ECG signals are needed to assist clinicians in more efficiently diagnosing arrhythmia and myocardial infarction.
Does an ensemble deep learning model combining CNN and SLAP accurately classify ECG signals for arrhythmia diagnosis?
Does an ensemble deep learning model combining CNN and SLAP accurately classify ECG signals for arrhythmia diagnosis?
An ensemble deep learning approach combining CNN and SLAP achieved high accuracy (>99%) in classifying ECG signals for arrhythmia detection.
High accuracy supports AI-assisted ECG monitoring in practice; extends ensemble DL methods for arrhythmia classification research.
An electrocardiograph (ECG) reflects the health of the human heart and is used to help diagnose arrhythmia and myocardial infarction(MI) in clinical practice. Early diagnosis of arrhythmia helps implement preventive measures and plays a crucial role in saving a patient's life. With the increasing demand of clinicians for ECG analysis technology, intelligent detection and diagnosis of ECG signals has become a more efficient means to assist physicians in diagnosing cardiovascular diseases. This paper introduces an ECG diagnosis approach based on an ensemble deep learning combination of CNN(convolutional neural network) and SLAP(stacked-long short term memory architecture for prediction) architecture. ECG data is denoised and further divided into single heartbeats to achieve data standardization and sample diversity. Adam optimizer and BCEwithlogitsloss multi-classification loss function were used to enhance the model effect, and the system achieved the classification effect of 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity in MIT-BIH standard database classification. It also shows good generalization ability on the Tianchi data set.
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Jiang et al. (2022) studied Arrhythmia and myocardial infarction. Ensemble deep learning combination of CNN and SLAP architecture was evaluated on Classification effect (average accuracy). An ensemble deep learning approach combining CNN and SLAP architecture achieved 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity for ECG classification on the MIT-BIH database.
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