Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG
Article
Sharma, Manish and Acharya, U. Rajendra. 2021. "Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG." Cognitive Neurodynamics. 15 (4), pp. 661-674. https://doi.org/10.1007/s11571-020-09655-w
Article Title | Automated detection of schizophrenia using optimal wavelet-based l1 norm features extracted from single-channel EEG |
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ERA Journal ID | 3179 |
Article Category | Article |
Authors | Sharma, Manish and Acharya, U. Rajendra |
Journal Title | Cognitive Neurodynamics |
Journal Citation | 15 (4), pp. 661-674 |
Number of Pages | 14 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 1871-4080 |
1871-4099 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11571-020-09655-w |
Web Address (URL) | https://link.springer.com/article/10.1007/s11571-020-09655-w |
Abstract | Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature. |
Keywords | Computer-aided diagnosis (CAD); KNN; Schizophrenia |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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