Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data

Yilin Song, Jonathan Viventi, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We have developed flexible, active, multiplexed recording devices for high resolution recording over large, clinically relevant areas in the brain. While this technology has enabled a much higher-resolution view of the electrical activity of the brain, the analytical methods to process, categorize and respond to the huge volumes of data produced by these devices have not yet been developed. In our previous work, we applied advanced video analysis techniques to segment electrographic spikes, extracted features from the identified segments, and then used clustering methods (particularly Dirichlet Process Mixture models) to group similar spatiotemporal spike patterns. From this analysis, we were able to identify common spike motion patterns. In this paper, we explored the possibility of detecting and predicting seizures in this dataset using the Hidden Markov Model (HMM) to characterize the temporal dynamics of spike cluster labels. HMM and other supervised learning methods are united under the same framework to perform seizure detection and prediction. These methods have been applied to in-vivo feline seizure recordings and yielded promising results.

Original languageEnglish (US)
Title of host publication2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013500
DOIs
StatePublished - Feb 11 2016
EventIEEE Signal Processing in Medicine and Biology Symposium - Philadelphia, United States
Duration: Dec 12 2015 → …

Conference

ConferenceIEEE Signal Processing in Medicine and Biology Symposium
CountryUnited States
CityPhiladelphia
Period12/12/15 → …

Fingerprint

Hidden Markov models
Cluster Analysis
Brain
Seizures
Supervised learning
Labels
Equipment and Supplies
Felidae
Learning
Technology

ASJC Scopus subject areas

  • Biomedical Engineering
  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

Cite this

Song, Y., Viventi, J., & Wang, Y. (2016). Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data. In 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings [7405420] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SPMB.2015.7405420

Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data. / Song, Yilin; Viventi, Jonathan; Wang, Yao.

2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7405420.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Song, Y, Viventi, J & Wang, Y 2016, Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data. in 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings., 7405420, Institute of Electrical and Electronics Engineers Inc., IEEE Signal Processing in Medicine and Biology Symposium, Philadelphia, United States, 12/12/15. https://doi.org/10.1109/SPMB.2015.7405420
Song Y, Viventi J, Wang Y. Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data. In 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 7405420 https://doi.org/10.1109/SPMB.2015.7405420
Song, Yilin ; Viventi, Jonathan ; Wang, Yao. / Seizure detection and prediction through clustering and temporal analysis of micro electrocorticographic data. 2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016.
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