Conference contribution
(Conference Contribution)


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


Publication Details
Author(s): Richer R, Groh B, Blank P, Dorschky E, Martindale C, Klucken J, Eskofier B
Publication year: 2016
Conference Proceedings Title: IEEE EMBS 13th Annual International Body Sensor Networks Conference
Language: English

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.



How to cite
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 EMBS 13th Annual International Body Sensor Networks Conference.

MLA: Richer, Robert, et al. "Unobtrusive Real-time Heart Rate Variability Analysis for the Detection of Orthostatic Dysregulation." Proceedings of the IEEE EMBS 13th Annual International Body Sensor Networks Conference 2016.

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