A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection

@article{Page2015AFM,
  title={A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection},
  author={Adam Page and Chris Sagedy and Emily Smith and Nasrin Attaran and Tim Oates and Tinoosh Mohsenin},
  journal={IEEE Transactions on Circuits and Systems II: Express Briefs},
  year={2015},
  volume={62},
  pages={109-113}
}
This brief presents a low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection. Various features and classifiers were explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. Additionally, algorithmic and hardware optimizations were identified to further improve performance. The classifiers studied include $k$-nearest neighbor, support vector machines, naïve… CONTINUE READING

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Key Quantitative Results

  • All feature and classifier pairs were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on ten patients.
  • For example, on average, the low-complexity five-feature/ch + LR setup achieves a 100% onset sensitivity and 95% window sensitivity with 0.45 ± 0.56 false alarms/hr.
  • Five low-complexity features were identified that could be derived from the EEG data to effectively classify windows of samples with an accuracy value of over 80% for data collected from ten patients.

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