A novel unsupervised spike sorting algorithm for intracranial EEG.
A novel unsupervised spike sorting algorithm for intracranial EEG.
复制标题
一种新颖的颅内脑电图无监督尖峰排序算法。
DOI：
10.1109/iembs.2011.6091860
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Agarwal,R
中科院分区：
文献类型：
--
作者：
Yadav,R;Shah,AK;Loeb,JA;Swamy,MNS;Agarwal,R
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.
