Fast and Efficient Rejection of Background Waveforms In Interictal EEG

Elham Bagheri1, Jin Jing1, Justin Dauwels1, M. Brandon Westover2, Sydney S. Cash3

  • 1Nanyang Technological University
  • 2Massachusetts General Hospital, and Harvard Medical school
  • 3Massachusetts General Hospital and Harvard Medical school

Details

Category

Technical Session: Poster

Theme

Bio Imaging and Signal Processing

Sessions

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

Processing of Electro-Physiological Signals

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Abstract

Automated annotation of electroencephalograms (EEG) of epileptic patients is important in diagnosis and management of epilepsy. Epilepsy is often associated with the presence of epileptiform transients (ET) in the EEG. To develop an efficient ET detector, a vast amount of data is required to train and evaluate the performance of the detector. Interictal EEG data contains mostly background waveforms, since ETs only occur occasionally in most patients. In order to detect ETs in an automated fashion, it is meaningful to first try to eliminate most background waveforms by means of simple, fast classifiers. The remaining waveforms can in a following step be processed by more sophisticated and computationally demanding classification algorithms, such as deep learning systems. In this study, we design a cascade of simple thresholding steps to reject most background waveforms in interictal EEG, while maintaining most ETs. Several simple and quick-to-compute EEG features are chosen. By thresholding these features in consecutive steps, background waveforms are rejected sequentially. In our numerical experiments, a cascade of 10 steps is able to reject 98.65% of all background segments in the dataset, while preserving 90.6% of the ETs.

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