Key result
A logistic regression classifier with five features per channel achieved an average F1 score of 91% and 100% onset sensitivity for personalized seizure detection in 10 patients.
A flexible multichannel EEG feature extractor using logistic regression provides high accuracy and low power consumption for personalized seizure detection.
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May support low-power personalized EEG wearables in epilepsy; hypothesis-generating and requires larger validation before clinical adoption.
Page et al. (2014) studied Seizures (n=10). Logistic regression classifier with five features per channel vs. k-nearest neighbor, support vector machines, and naïve Bayes classifiers was evaluated on F1 score and onset sensitivity. A logistic regression classifier with five features per channel achieved an average F1 score of 91% and 100% onset sensitivity for personalized seizure detection in 10 patients.
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