Literature DB >> 24111118

Classification of schizophrenia using Genetic Algorithm-Support Vector Machine (GA-SVM).

Ming-Hsien Hiesh, Yan-Yu Lam Andy, Chia-Ping Shen, Wei Chen, Feng-Shen Lin, Hsiao-Ya Sung, Jeng-Wei Lin, Ming-Jang Chiu, Feipei Lai.   

Abstract

Recently, Event-Related Potential (ERP) has being the most popular method in evaluating brain waves of schizophrenia patients. ERP is one of the electroencephalography (EEG), which is measured the change of brain waves after giving patients certain stimulations instead of resting state. However, with traditional statistical analysis method, both P50 and MMN showed significant difference between controls and patients but not in Gamma band. Gamma band is a 30-50 Hz auditory stimulation which had been suggested may be abnormal in schizophrenia patients. Our data are recruited from 5 schizophrenia patients and 5 controls in National Taiwan University Hospital have been tested with this platform. The results showed that detection rate is 88.24% and we also analyzed the importance of features, including Standard Deviation (SD) and Total Variation (TotalVar) in different stage of wavelet transform. Therefore, this proposed methodology could serve as a valuable clinical decision support for physiologists in evaluating schizophrenia.

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Year:  2013        PMID: 24111118     DOI: 10.1109/EMBC.2013.6610931

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  1 in total

1.  Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.

Authors:  Sai Krishna Tikka; Bikesh Kumar Singh; S Haque Nizamie; Shobit Garg; Sunandan Mandal; Kavita Thakur; Lokesh Kumar Singh
Journal:  Indian J Psychiatry       Date:  2020-05-15       Impact factor: 1.759

  1 in total

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