Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series

Details

Event 10th EAI International Conference on Pervasive Computing Technologies for Healthcare

Authors

Jonathan Rubin
Rui Abreu
Shane Ahern
Hoda Eldardiry
Bobrow, Daniel G.
Technical Publications
May 16th 2016
This paper presents results of analysis performed on a physiologic time-series dataset that was collected from a wearable ECG monitoring system worn by individuals who suffer from panic disorder. Models are constructed and evaluated for distinguishing between pathologic and non-pathologic states, including panic (during panic attack), pre-panic (preceding panic attack) and non-panic (outside panic attack window). The models presented use data fusion to combine both traditional time and frequency domain heart rate variability analysis together with nonlinear/complexity analysis. The best performing model is shown to be a random forest classifier that achieves an accuracy of 97.2% and 90.7% for recognizing states of panic and pre-panic, respectively. The models presented have application in pervasive and ubiquitous mobile and wearable health management systems.

Citation

Rubin, J.; Abreu, R.; Ahern, S.; Eldardiry, H.; Bobrow, D. G. Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series. 10th EAI International Conference on Pervasive Computing Technologies for Healthcare.; Cancun, Mexico. Date of Talk: 2016-05-16

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