Single signal seizure detection algorithms suffer from high false positive rates. We have found a set of signals which can be easily monitored by a wristworn device and which produce a distinctive pattern during seizure for patients in an epilepsy monitoring unit (EMU). This pattern is much less likely to be reproduced by nonseizure events in the patient's daily life than are changes in heart rate alone. We collected 108 hours of data from three EMU patients who suffered a combined total of seven seizures, then developed a time series analysis/pattern recognition based algorithm which distinguishes the seizures from nonseizure events with 100% accuracy.
|Title of host publication
|2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Nov 4 2015
|37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015 → Aug 29 2015
|37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
|8/25/15 → 8/29/15
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics