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. 2017 Aug 24;7(1):9270.
doi: 10.1038/s41598-017-09544-z.

Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias

Serkan Kiranyaz  1 , Turker Ince  2 , Moncef Gabbouj  3
Affiliations

Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias

Serkan Kiranyaz et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Normal (N) vs. Abnormal (S and V) beats from different subjects in MIT-BIH dataset.
Figure 2
Figure 2
(a) Modeling common causes of cardiac arrhythmia in the signal domain by a degrading system. (b) A symbolic illustration of an abnormal S beat synthesis for Person-Y using the degrading system designed from the ECG data of Patient-X. (c) Illustration of the overall system, where a dedicated CNN is trained by Back-Propagation over the training dataset created for Person-X (top). Once the 1D CNN is trained, it can then be used as a continuous cardiac health monitoring and advance warning system (bottom) for Person-X.
Figure 3
Figure 3
(a) Average normal beat (ANB) selections for Subjects 114 (left) and 124 (right). (b) Real beats and their most-similar synthesized beats of some subjects in the test partition. In both single-beat (upper block) and beat-trio (lower block) representations the bottom and top sub-plots show the real and the synthesized beats, respectively.
Figure 4
Figure 4
The creation of the training dataset for Person-X using a limited number of real N-beats.
Figure 5
Figure 5
The system overflow of the proposed solution in an illustrative Client/Server application.
See this image and copyright information in PMC

References

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