Dataset Information

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.

SUBMITTER: Kiranyaz S

PROVIDER: S-EPMC5571226 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias.

Kiranyaz Serkan S Ince Turker T Gabbouj Moncef M

Scientific reports 20170824 1

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 mo ...[more]

PMID: 28839215

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Project description:Background In patients with end-stage kidney disease, sudden cardiac death is more frequent after a long interdialytic interval, within 6 hours after the end of a hemodialysis session. We hypothesized that the occurrence of paroxysmal arrhythmias is associated with changes in heart rate and heart rate variability in different phases of hemodialysis. Methods and Results We conducted a prospective ancillary study of the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease cohort. Continuous ECG monitoring was performed using an ECG patch, and short-term heart rate variability was measured for 3 minutes every hour (by root mean square of the successive normal-to-normal intervals, spectral analysis, Poincaré plot, and entropy), up to 300 hours. Out of enrolled participants (n=28; age 54±13 years; 57% men; 96% black; 33% with a history of cardiovascular disease; left ventricular ejection fraction 70±9%), arrhythmias were detected in 13 (46%). Nonsustained ventricular tachycardia occurred more frequently during/posthemodialysis than pre-/between hemodialysis (63% versus 37%, P=0.015). In adjusted for cardiovascular disease time-series analysis, nonsustained ventricular tachycardia was preceded by a sudden heart rate increase (by 11.2 [95% CI 10.1-12.3] beats per minute; P<0.0001). During every-other-day dialysis, root mean square of the successive normal-to-normal intervals had a significant circadian pattern (Mesor 10.6 [ 95% CI 0.9-11.2] ms; amplitude 1.5 [95% CI 1.0-3.1] ms; peak at 02:01 [95% CI 20:22-03:16] am; P<0.0001), which was replaced by a steady worsening on the second day without dialysis (root mean square of the successive normal-to-normal intervals -1.41 [95% CI -1.67 to -1.15] ms/24 h; P<0.0001). Conclusions Sudden increase in heart rate during/posthemodialysis is associated with nonsustained ventricular tachycardia. Every-other-day hemodialysis preserves circadian rhythm, but a second day without dialysis is characterized by parasympathetic withdrawal.

Dataset Information

Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias.

Publications

Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias.

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