Shift-invariant waveform learning on epileptic ECoG

• URL: http://arxiv.org/abs/2108.03177v1
• Date: Fri, 6 Aug 2021 15:47:17 GMT
• Title: Shift-invariant waveform learning on epileptic ECoG
• Authors: Carlos H. Mendoza-Cardenas and Austin J. Brockmeier
• Abstract summary: Seizure detection algorithms must discriminate abnormal activity associated with a seizure from normal neural activity in a variety of conditions. We apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of waveforms. We find recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful.
• Score: 1.7948767405202701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a subsequent seizure (preictal) versus non-seizure segments (interictal). To find these waveforms we apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of prototypical waveforms. The frequency of the cluster labels from the codebooks is then used to train a binary classifier that predicts the class (preictal or interictal) of a test ECoG segment. We use the Matthews correlation coefficient to evaluate the performance of the classifier and the quality of the codebooks. We found that our method finds recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful.

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