Time, frequency & complexity analysis for recognizing panic states from physiologic time-series

Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 81-88, 2016.

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Abstract:

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...More

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