Clustering of Interictal Spikes By Dynamic Time Warping and Affinity Propagation

Jin Jing1, John Thomas2, Justin Dauwels2, M. Brandon Westover3, Sydney S. Cash4

  • 1Nanyang Technological University, School of Electrical and Electronic Engineering
  • 2Nanyang Technological University
  • 3Massachusetts General Hospital, and Harvard Medical school
  • 4Massachusetts General Hospital and Harvard Medical school



Technical Session: Poster


Bio Imaging and Signal Processing


13:30 - 15:30 | Tue 22 Mar | Poster Area J | BISP-P1

Processing of Electro-Physiological Signals

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Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a better understanding of the various spike shapes. In this paper, we develop several approaches to extract exemplars of spikes. We generate spike exemplars by applying clustering algorithms to a database of spikes from 12 patients. As similarity measures for clustering, we consider the Euclidean distance and Dynamic Time Warping (DTW). We assess two clustering algorithms, namely, K-means clustering and affinity propagation. The clustering methods are compared based on the mean squared error, and the similarity measures are assessed based on the number of generated spike clusters. Affinity propagation with DTW is shown to be the best combination for clustering epileptic spikes, since it generates fewer spike templates and does not require to pre-specify the number of spike templates.

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