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.
@article{arxiv.2108.03177,
title = {Shift-invariant waveform learning on epileptic ECoG},
author = {Carlos H. Mendoza-Cardenas and Austin J. Brockmeier},
journal= {arXiv preprint arXiv:2108.03177},
year = {2021}
}
Comments
To be published in the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Corrected band number for patient Study012 in Table IV