Abstract
Each year more than 7 million people die from cardiac arrhythmias. Yet no robust solution exists today to detect such heart anomalies right at the moment they occur. The purpose of this study was to design a personalized health monitoring system that can detect early occurrences of arrhythmias from an individual’s electrocardiogram (ECG) signal. We first modelled the common causes of arrhythmias in the signal domain as a degradation of normal ECG beats to abnormal beats. Using the degradation models, we performed abnormal beat synthesis which created potential abnormal beats from the average normal beat of the individual. Finally, a Convolutional Neural Network (CNN) was trained using real normal and synthesized abnormal beats. As a personalized classifier, the trained CNN can monitor ECG beats in real time for arrhythmia detection. Over 34 patients’ ECG records with a total of 63,341 ECG beats from the MIT-BIH arrhythmia benchmark database, we have shown that the probability of detecting one or more abnormal ECG beats among the first three occurrences is higher than 99.4% with a very low false-alarm rate.
Highlights
IntroductionUsing the library of filters over an average normal beat, one can generate a set of potential abnormal ECG beats of that person and a dedicated classifier can be trained in advance to be used as an early detection system for cardiac arrhythmia
In order to address this drawback efficiently and propose a reliable solution for the early detection of ECG anomalies, in this study we proposed an abnormal beat synthesis (ABS) approach, which can artificially create potential abnormal beats for a healthy individual using a library of filters applied to regular normal beats
The personalized ECG monitoring system presented in this study for the early detection of cardiac arrhythmias performs three prior and one-time operations: 1) Creation of the abnormal beat synthesis (ABS) filter library by performing regularized Least-Squares optimization, 2) Creation of the personalized training dataset via synthesis of potential abnormal beats using the ABS filter library over the person’s average normal beat (ANB), and 3) Training a dedicated Convolutional Neural Network (CNN) for that person over the training dataset created
Summary
Using the library of filters over an average normal beat, one can generate a set of potential abnormal ECG beats of that person and a dedicated classifier can be trained in advance to be used as an early detection system for cardiac arrhythmia
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