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.

A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset

Petrillo, Alberto;Santini, Stefania;
2022

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/901252
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