Automatic identification of schizophrenia using EEG signals based on discrete wavelet transform and RLNDiP technique with ANN

作者：

Highlights：

• Novel RLNDip technique for automatic identification of schizophrenia using EEG signals is proposed.

• A fusion approach of DWT with RLNDiP technique is introduced in this work.

• Analysis of EEG signals in different brain rhythms is evaluated.

• Obtained results conclude that alpha rhythm achieved a better classification performance using ANN.

摘要

•Novel RLNDip technique for automatic identification of schizophrenia using EEG signals is proposed.•A fusion approach of DWT with RLNDiP technique is introduced in this work.•Analysis of EEG signals in different brain rhythms is evaluated.•Obtained results conclude that alpha rhythm achieved a better classification performance using ANN.

论文关键词：Electroencephalogram (EEG),Relaxed local neighbour difference pattern (RLNDiP),Discrete wavelet transform (DWT),Artificial neural network (ANN),Schizophrenia (ScZ)

论文评审过程：Received 19 April 2021, Revised 21 September 2021, Accepted 12 November 2021, Available online 25 November 2021, Version of Record 29 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116230