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Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias

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Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias. / Kiranyaz, Serkan; Ince, Turker; Gabbouj, Moncef.

julkaisussa: Scientific Reports, Vuosikerta 7, Nro 1, 9270, 01.12.2017.

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Kiranyaz, Serkan ; Ince, Turker ; Gabbouj, Moncef. / Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias. Julkaisussa: Scientific Reports. 2017 ; Vuosikerta 7, Nro 1.

Bibtex - Lataa

@article{32a5bb69ebce4922b630007bfbfed410,
title = "Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias",
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.",
author = "Serkan Kiranyaz and Turker Ince and Moncef Gabbouj",
note = "EXT={"}Ince, Turker{"} EXT={"}Kiranyaz, Serkan{"}",
year = "2017",
month = "12",
day = "1",
doi = "10.1038/s41598-017-09544-z",
language = "English",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias

AU - Kiranyaz, Serkan

AU - Ince, Turker

AU - Gabbouj, Moncef

N1 - EXT="Ince, Turker" EXT="Kiranyaz, Serkan"

PY - 2017/12/1

Y1 - 2017/12/1

N2 - 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.

AB - 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.

U2 - 10.1038/s41598-017-09544-z

DO - 10.1038/s41598-017-09544-z

M3 - Article

VL - 7

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 9270

ER -