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KMID : 1144120220120040433
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Biomedical Engineering Letters
2022 Volume.12 No. 4 p.433 ~ p.444
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A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset
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Liotto Carmine
Petrillo Alberto
Santini Stefania
Toscano Gianluca
Tufano Vincenza
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Abstract
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The accurate analysis of Electrocardiogram waveform plays a crucial role for supporting cardiologist in detecting and diagnosing the heartbeat disorders. To improve their detection accuracy, this work is devoted to the design of a novel classification algorithm which is composed of a cascade of two convolutional neural network (CNN), i.e a Binary CNN allowing the detection of the arrhythmic heartbeat and a Multiclass CNN able to recognize the specific disorder. Moreover, by combining the cascade architecture solution with a rule-based data splitting, which leverages the subject-exclusive and balances among the classes criteria, it is possible predicting the health status of unseen patients. Numerical results, carried out considering Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database, disclose a classification accuracy of 96.2%. Finally, a cross-database performance evaluation and a comparison analysis w.r.t. the current state-of-art further disclose the effectiveness and the efficiency of the proposed solution, as well as its benefits in terms of patient health status prediction.
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KEYWORD
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Cardiovascular disorders, ECG, Arrhythmia classification, Convolutional neural network (CNN), Multiclass CNN cascade, Subject-exclusive criteria
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