t4-pz
Chronic pain detection from resting-state raw EEG signals using improved feature selection
Li, Jean, De Ridder, Dirk, Adhia, Divya, Hall, Matthew, Deng, Jeremiah D.
We present an automatic approach that works on resting-state raw EEG data for chronic pain detection. A new feature selection algorithm - modified Sequential Floating Forward Selection (mSFFS) - is proposed. The improved feature selection scheme is rather compact but displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an external dataset that contains different types of chronic pain
2306.15194
Country:
- Asia > China (0.04)
- Oceania > New Zealand > South Island > Otago > Dunedin (0.04)
- North America > United States > New York > New York County > New York City (0.04)
- (2 more...)
Industry:
- Health & Medicine > Therapeutic Area > Neurology (1.00)
- Health & Medicine > Therapeutic Area > Musculoskeletal (1.00)
- Health & Medicine > Consumer Health (1.00)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)