title: A novel unsupervised spike sorting algorithm for intracranial EEG.
abstract: This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.
authors.affiliation: Center for Signal Processing and Communications, Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd West, Montreal, QC H3G 1M8, Canada. r_yadav@encs.concordia.ca
authors.forename: R
authors.initials: R
authors.lastname: Yadav
authors.forename: A K
authors.initials: AK
authors.lastname: Shah
authors.forename: J A
authors.initials: JA
authors.lastname: Loeb
authors.forename: M N S
authors.initials: MN
authors.lastname: Swamy
authors.forename: R
authors.initials: R
authors.lastname: Agarwal
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D016000:Cluster Analysis; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology; D006801:Humans; D025341:Principal Component Analysis