A new localization method for epileptic seizure onset zones based on time-frequency and clustering analysis
作者：
Highlights：
• The Stockwell entropy based on Hilbert transform detects events of interest (EoIs) effectively compared with simple Hilbert transform by accurately detecting both the EoI and non-EoI.
• The power method based on Shannon-entropy-based complex Morlet wavelet transform obtains channels of interests with lower computational complexity than the power spectral density method based on SECMWT does.
• The adaptive-genetic-algorithm-based matching pursuit (AGA-MP) integrated with the k-medoids clustering method is found to detect high-frequency oscillations (HFOs) more effectively than the AGA-MP method by discerning HFOs from normal activity and artifacts.
• The devised new localization method has superiority in improving the localization performance (i.e. sensitivity and specificity) over some existing methods.
摘要
•The Stockwell entropy based on Hilbert transform detects events of interest (EoIs) effectively compared with simple Hilbert transform by accurately detecting both the EoI and non-EoI.•The power method based on Shannon-entropy-based complex Morlet wavelet transform obtains channels of interests with lower computational complexity than the power spectral density method based on SECMWT does.•The adaptive-genetic-algorithm-based matching pursuit (AGA-MP) integrated with the k-medoids clustering method is found to detect high-frequency oscillations (HFOs) more effectively than the AGA-MP method by discerning HFOs from normal activity and artifacts.•The devised new localization method has superiority in improving the localization performance (i.e. sensitivity and specificity) over some existing methods.
论文关键词：Epilepsy,Seizure onset zones,High-frequency oscillations,Time-frequency analysis,Clustering analysis
论文评审过程：Received 18 June 2020, Accepted 2 October 2020, Available online 6 October 2020, Version of Record 21 October 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107687
