Conference contribution
(Conference Contribution)


Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature


Publication Details
Author(s): Leutheuser H, Tobola A, Anneken L, Gradl S, Arnold M, Lang N, Achenbach S, Eskofier B
Publisher: Institute of Electrical and Electronics Engineers Inc.
Publication year: 2015
Conference Proceedings Title: 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
ISBN: 9781467372015

Event details
Event: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Event location: Cambridge, USA
Start date of the event: 09/06/2015
End date of the event: 12/06/2015
Language: English

Abstract
Far too many people are dying from stroke or other heart related diseases each year. Early detection of abnormal heart rhythm could trigger the timely presentation to the emergency department or outpatient unit. Smartphones are an integral part of everyone;s life and they form the ideal basis for mobile monitoring and real-time analysis of signals related to the human heart. In this work, we investigated the performance of arrhythmia classification systems using only features calculated from the time instances of individual heart beats. We built a sinusoidal model using N (N = 10, 15, 20) consecutive RR intervals to predict the (N+1)th RR interval. The integration of the innovative sinusoidal regression feature, together with the amplitude and phase of the proposed sinusoidal model, led to an increase in the mean class-dependent classification accuracies. Best mean class-dependent classification accuracies of 90% were achieved using a Naïve Bayes classifier. Well-performing realtime analysis arrhythmia classification algorithms using only the time instances of individual heart beats could have a tremendous impact in reducing healthcare costs and reducing the high number of deaths related to cardiovascular diseases.


How to cite
APA: Leutheuser, H., Tobola, A., Anneken, L., Gradl, S., Arnold, M., Lang, N.,... Eskofier, B. (2015). Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature. In 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Cambridge, USA: Institute of Electrical and Electronics Engineers Inc..

MLA: Leutheuser, Heike, et al. "Arrhythmia classification using RR intervals: Improvement with sinusoidal regression feature." Proceedings of the 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, USA Institute of Electrical and Electronics Engineers Inc., 2015.

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