Multi-scale Weighted Inherent Fuzzy Entropy for EEG Biomarkers

Entropy has been widely investigated as an effective metric to evaluate the dynamic complexity of signals. EEG is biological signals that contain rich complex dynamics. Transforming the information encoded in the rich dynamics embedded within EEG into appropriate biomarkers with discriminatory powers is important for event detection. It has broad prospects in a wide range of applications including medical diagnosis, therapy, and rehabilitation. This paper proposes a new entropy-based measure, Multi-scale Weighted Inherent Fuzzy Entropy (WIFEn), as an effective EEG biomarker for improving event detection performance. WIFEn first extracts Inherent Mode Functions (IMFs) using the Empirical Mode Decomposition method, then uses a weighted sum scheme to fuse the fuzzy entropy metrics calculated on each IMF. Finally, the multi-scale variation accounts for the multi-timescale dynamics inherent in EEG signals. Since EEG signals are a superposition of series of oscillations where information embedded in these oscillations is useful for estimating signal complexity, the aforementioned decomposition, and weighted sum procedures can improve the estimation results. The proposed method is tested with three entropy-based metrics for two tasks. The first task is eye-open and eye-closed detection with resting state EEG signals recorded from 10 subjects; while the second task is seizure detection for 8 epilepsy patients. The results indicate that the multi-scale WIFEn provides a better discriminatory power that improves detection performance than classic entropy-based measures, with an averaged improvement of 13.7% (p-value < 0.05) for resting-state classification and 5.9% (p-value < 0.05) for seizure detection.