Unobtrusive Real-time Heart Rate Variability Analysis for the Detection of Orthostatic Dysregulation

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autor(en): Richer R, Groh B, Blank P, Dorschky E, Martindale C, Klucken J, Eskofier B
Herausgeber: IEEE
Jahr der Veröffentlichung: 2016
Tagungsband: 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
ISBN: 978-1-5090-3087-3
ISSN: 2376-8894
Sprache: Englisch


Abstract


The possibilities for wearable health care technology to improve the quality of life for chronic disease patients has been increasing within recent years. For instance, unobtrusive cardiac monitoring can be applied to people suffering from a disorder of the autonomic nervous system (ANS) which show a significantly lower heart rate variability (HRV) than healthy people. Although recent work presented solutions to analyze this relationship, they did not perform it during daily life situations. For that reason, this work presents a system for a real-time analysis of the user's HRV on an Android-based mobile device throughout the day. The system was used for the detection of an orthostatic dysregulation which can be an indicator for a disorder of the ANS. Measures for HRV analysis were computed from acquired ECG data and compared before and after a posture change. For triggering the HRV analysis, an IMU-based algorithm which detects stand up events was developed. As a proof of concept for an automatic assessment of an orthostatic dysregulation, a classification based on the derived HRV measures was performed. The performance of the stand up detection was evaluated in the first part of this study. The second part was conducted for the evaluation of the derived HRV measures and involved healthy subjects as well as patients with idiopathic Parkinson's Disease. The results of the evaluation showed a recognition rate of 90.0% for the stand up detection algorithm. Furthermore, a clear difference in the change of HRV measures between the two groups before and after standing up was observed. The classification provided an accuracy of 96.0%, and a sensitivity of 93.3%. The results demonstrated the possibility of unobtrusive HRV monitoring during daily life situations.



FAU-Autoren / FAU-Herausgeber

Blank, Peter
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Dorschky, Eva
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Groh, Benjamin
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Klucken, Jochen Prof. Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Martindale, Christine
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Richer, Robert
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


Zitierweisen

APA:
Richer, R., Groh, B., Blank, P., Dorschky, E., Martindale, C., Klucken, J., & Eskofier, B. (2016). Unobtrusive Real-time Heart Rate Variability Analysis for the Detection of Orthostatic Dysregulation. In IEEE (Eds.), 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). San Francisco, CA, US.

MLA:
Richer, Robert, et al. "Unobtrusive Real-time Heart Rate Variability Analysis for the Detection of Orthostatic Dysregulation." Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA Ed. IEEE, 2016.

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Zuletzt aktualisiert 2018-19-04 um 03:31