Abstract
We describe a segmentation algorithm for extracellular microelectrode recordings identifying segments with equal power. The observed temporal correlation of the microelectrode data was accounted for by estimating the variance of the autocovariance of the segments. The iterative method of comparing segments and determining abrupt changes in the power was validated on synthetic signals and microelectrode recording data acquired at different neuronal structures along the stereotactic trajectory in patients with Parkinson disease.
Original language | English (US) |
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Pages (from-to) | 2475-2478 |
Number of pages | 4 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 3 |
State | Published - 2003 |
Event | A New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico Duration: Sep 17 2003 → Sep 21 2003 |
Keywords
- Extracellular microelectrode recording
- Parkinson's disease
- Segmentation
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics