Automated seizure detection using limited-channel EEG and non-linear dimension reduction

Javad Birjandtalab, Maziyar Baran Pouyan, Diana Cogan, Mehrdad Nourani, Jay Harvey

Research output: Contribution to journalArticlepeer-review

99 Scopus citations


Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of both channel selection and nonlinear dimension reduction for accurate automatic seizure detection. We first extract the frequency domain features from the full-channel EEG signals. Then, we use a random forest algorithm to determine which channels contribute the most in discriminating seizure from non-seizure events. Next, we apply a non-linear dimension reduction technique to capture the relationship among data elements and map them in low dimension. Finally, we apply a KNN classifier technique to discriminate between seizure and non-seizure events. The experimental results for 23 patients show that our proposed approach outperforms other techniques in terms of accuracy. It also visualizes long-term data in 2D to enhance physician cognition of occurrence and disease progression.

Original languageEnglish (US)
Pages (from-to)49-58
Number of pages10
JournalComputers in Biology and Medicine
StatePublished - Mar 1 2017


  • Channel selection
  • EEG signals
  • Feature extraction
  • Nonlinear dimension reduction
  • Random forest
  • Seizure detection.

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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