Seizure detection using power spectral density via hyperdimensional computing

Research output: Contribution to journalConference articlepeer-review

Abstract

Hyperdimensional (HD) computing holds promise for classifying two groups of data. This paper explores seizure detection from electroencephalogram (EEG) from subjects with epilepsy using HD computing based on power spectral density (PSD) features. Publicly available intra-cranial EEG (iEEG) data collected from 4 dogs and 8 human patients in the Kaggle seizure detection contest are used in this paper. This paper explores two methods for classification. First, few ranked PSD features from small number of channels from a prior classification are used in the context of HD classification. Second, all PSD features extracted from all channels are used as features for HD classification. It is shown that for about half the subjects small number features outperform all features in the context of HD classification, and for the other half, all features outperform small number of features. HD classification achieves above 95% accuracy for six of the 12 subjects, and between 85-95% accuracy for 4 subjects. For two subjects, the classification accuracy using HD computing is not as good as classical approaches such as support vector machine classifiers.

Original languageEnglish (US)
Pages (from-to)7858-7862
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
StatePublished - Jun 6 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 11 2021

Bibliographical note

Funding Information:
This paper was supported in parts by NSF grant CCF-1814759 and by the Chinese Scholarship Council (CSC).

Publisher Copyright:
© 2021 IEEE

Keywords

  • Hyperdimensional (HD) computing
  • Power spectral density (PSD)
  • Seizure detection

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