Affordable Access

deepdyve-link
Publisher Website

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

Authors
  • Baud, Maxime O1, 2
  • Kleen, Jonathan K2
  • Anumanchipalli, Gopala K1
  • Hamilton, Liberty S1
  • Tan, Yee-Leng2, 3
  • Knowlton, Robert2
  • Chang, Edward F1
  • 1 Department of Neurological surgery, University of California, San Francisco, California.
  • 2 Department of Neurology, University of California, San Francisco, California.
  • 3 National Neuroscience Institute, Singapore, Singapore. , (Singapore)
Type
Published Article
Journal
Neurosurgery
Publication Date
Oct 01, 2018
Volume
83
Issue
4
Pages
683–691
Identifiers
DOI: 10.1093/neuros/nyx480
PMID: 29040672
Source
Medline
Language
English
License
Unknown

Abstract

Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms. To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition. We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected interictal epileptiform discharges in time and space (specific electrodes), respectively. We used convolutive NNMF, a refined algorithm, to add a temporal dimension to basis functions. The receiver operating characteristics for NNMF-based detection are close to the gold standard of human visual-based detection and superior to currently available alternative automated approaches (93% sensitivity and 97% specificity). The algorithm successfully identified thousands of interictal epileptiform discharges across a full day of neurophysiological recording and accurately summarized their localization into a single map. Adding a temporal window allowed for visualization of the archetypal propagation network of these epileptiform discharges. Unsupervised learning offers a powerful approach towards automated identification of recurrent pathological neurophysiological signals, which may have important implications for precise, quantitative, and individualized evaluation of focal epilepsy.

Report this publication

Statistics

Seen <100 times