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A hybrid decision support system for automatic detection of Schizophrenia using EEG signals

Computers in biology and medicine, 2022-02, Vol.141, p.105028-105028, Article 105028 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.105028 ;PMID: 34836626

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  • Title:
    A hybrid decision support system for automatic detection of Schizophrenia using EEG signals
  • Author: Khare, Smith K. ; Bajaj, Varun
  • Subjects: Algorithms ; Artificial neural networks ; Classification ; Classifiers ; Cognition ; Decision support systems ; Discriminant analysis ; EEG ; Electroencephalography ; Feature extraction ; Fractals ; Hallucinations ; Humans ; Hybrid systems ; Inspection ; Interviews ; Learning algorithms ; Machine learning ; Medical imaging ; Memory ; Mental disorders ; Neural networks ; Optimization ; Optimized extreme learning machine classifier ; Robust variational mode decomposition ; Schizophrenia ; Schizophrenia - diagnosis ; Segmentation ; Signal classification ; Signal Processing, Computer-Assisted ; Statistical analysis ; Suicides & suicide attempts ; Support Vector Machine ; Support vector machines ; Wavelet transforms
  • Is Part Of: Computers in biology and medicine, 2022-02, Vol.141, p.105028-105028, Article 105028
  • Description: Schizophrenia (SCZ) is a serious neurological condition in which people suffer with distorted perception of reality. SCZ may result in a combination of delusions, hallucinations, disordered thinking, and behavior. This causes permanent disability and hampers routine functioning. Trained neurologists use interviewing and visual inspection techniques for the detection and diagnosis of SCZ. These techniques are manual, time-consuming, subjective, and error-prone. Therefore, there is a need to develop an automatic model for SCZ classification. The aim of this study is to develop an automated SCZ classification model using electroencephalogram (EEG) signals. The EEG signals can capture the changes in neural dynamics of human cognition during SCZ. Based on the nature of the SCZ condition, the EEG signals must be examined. For accurate interpretation of EEG signals during SCZ, an automated model integrating a robust variational mode decomposition (RVMD) and an optimized extreme learning machine (OELM) classifier is developed. Traditional VMD suffers from noisy mode generation, mode duplication, under segmentation, and mode discarding. These problems are suppressed in RVMD by automating the selection of quadratic penalty factor (α) and a number of modes (L). The hyperparameters (HPM) of the OELM classifier are automatically selected to ensure maximum accuracy for each mode without overfitting or underfitting. For the selection of α and L in RVMD and HPM in the OELM classifier, a whale optimization algorithm is used. The root mean square error is minimized for RVMD and classification accuracy of each mode is maximized for the OELM classifier. The EEG signals of three conditions performing basic sensory tasks have been analyzed to detect SCZ. The Kruskal Wallis test is used to select different features extracted from the modes produced by RVMD. An OELM classifier is tested using a ten-fold cross-validation technique. An accuracy, precision, specificity, F-1 measure, sensitivity, and Cohen's Kappa of 92.93%, 93.94%, 91.06% 94.07%, 97.15%, and 85.32% are obtained. The third mode's chaotic features helped to capture the significant changes that occurred during the SCZ state. The findings of the RVMD-OELM-based hybrid decision support system can help neuro-experts for the accurate identification of SCZ in real-time scenarios. •RVMD technique for accurate interpretation of EEG signals during SCZ.•OELM for tuning of hyperparameters to improve the separability of SCZ and NHC.•Accurate selection of optimal number of modes for uniform decomposition.
  • Publisher: United States: Elsevier Ltd
  • Language: English
  • Identifier: ISSN: 0010-4825
    EISSN: 1879-0534
    DOI: 10.1016/j.compbiomed.2021.105028
    PMID: 34836626
  • Source: MEDLINE
    ProQuest Central
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