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Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning
Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore
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70% confidenceThe paper proposes a method for detecting schizophrenia from EEG signals using selected statistical moments of MFC coefficients and ensemble learning, achieving high accuracy and outperforming existing methods.
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Abstract
Key findings
- The proposed method achieved an accuracy of 92.5% in detecting schizophrenia from EEG signals.
- The use of MFC coefficients and ensemble learning improved the detection accuracy compared to traditional machine learning methods.
- The selected statistical moments of MFC coefficients were found to be effective in capturing the underlying patterns in EEG signals.
Keywords
ElectroencephalographySchizophrenia (object-oriented programming)Ensemble learningArtificial intelligencePattern recognition (psychology)Machine learning
Identifiers
- Journal
- Neuroinformatics
- Year
- 2024