Personalized EEG Feature Selection for Low-Complexity Seizure Monitoring

Genchang Peng, Mehrdad Nourani, Jay Harvey, Hina Dave

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Approximately, one third of patients with epilepsy are refractory to medical therapy and thus can be at high risk of injuries and sudden unexpected death. A low-complexity electroencephalography (EEG)-based seizure monitoring algorithm is critically important for daily use, especially for wearable monitoring platforms. This paper presents a personalized EEG feature selection approach, which is the key to achieve a reliable seizure monitoring with a low computational cost. We advocate a two-step, personalized feature selection strategy to enhance monitoring performances for each patient. In the first step, linear discriminant analysis (LDA) is applied to find a few seizure-indicative channels. Then in the second step, least absolute shrinkage and selection operator (LASSO) method is employed to select a discriminative subset of both frequency and time domain features (spectral powers and entropy). A personalization strategy is further customized to find the best settings (number of channels and features) that yield the highest classification scores for each subject. Experimental results of analyzing 23 subjects in CHB-MIT database are quite promising. We have achieved an average F-1 score of 88% with excellent sensitivity and specificity using not more than 7 features extracted from at most 3 channels.

Original languageEnglish (US)
Article number2150018
JournalInternational Journal of Neural Systems
Issue number8
StatePublished - Aug 2021


  • EEG channels
  • Feature selection
  • personalization
  • seizure monitoring
  • subset selection

ASJC Scopus subject areas

  • Computer Networks and Communications


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