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 is needed to meet clinicians' increasing demand for more efficient tools to assist in diagnosing cardiovascular diseases.
Does an ensemble deep learning combination of CNN and SLAP architecture accurately classify ECG signals for arrhythmia diagnosis?
Does an ensemble deep learning combination of CNN and SLAP architecture accurately classify ECG signals for arrhythmia diagnosis?
An ensemble deep learning model combining CNN and SLAP architecture achieved high accuracy in classifying ECG signals for arrhythmia detection.
Supports clinical ECG arrhythmia detection with ensemble deep learning; extends prior MIT-BIH benchmarks.
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 accuracy on the MIT-BIH standard database. 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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