EEG power spectrum analysis for schizophrenia during mental activity
Australasian Physical and Engineering Sciences in Medicine, ISSN: 1879-5447, Vol: 42, Issue: 3, Page: 887-897
2019
- 20Citations
- 47Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations20
- Citation Indexes20
- 20
- CrossRef1
- Captures47
- Readers47
- 47
Article Description
Cognitive dysfunction is a core defect for schizophrenia subjects. This is due to structural and functional abnormalities of the brain which can be determined using Electroencephalogram (EEG). The objective of this study is to analyze EEG in patients with schizophrenia using power spectral density during mental activity. The subjects included in this study are 52 schizophrenia subjects and 29 Normal subjects. EEG is recorded under resting condition and during mental activity. Two modified odd ball paradigms are designed to stimulate mental activity and named as stimulus 1 and stimulus 2. EEG signal is filtered using FIR band pass filter to extract delta, theta, alpha, and beta band EEG. This method measures powers of each band using Welch power spectral density method called absolute power. The absolute power of alpha band is low and beta band is high for schizophrenia subjects compared to normal subjects during rest and two stimuli. Student’s t-test is used to find the significant features (p < 0.05) at each recording condition. The significant features from each recording condition are used to classify Schizophrenia using both BPN and SVM classifier. SVM classifier is produced maximum sensitivity of 91% when features from all recording conditions are combined together. Thus this work concludes that the mental activity EEG supports for classifying Schizophrenia from normal and hence absolute band powers can be used as features to identify Schizophrenia.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85069902534&origin=inward; http://dx.doi.org/10.1007/s13246-019-00779-w; http://www.ncbi.nlm.nih.gov/pubmed/31364088; http://link.springer.com/10.1007/s13246-019-00779-w; https://dx.doi.org/10.1007/s13246-019-00779-w; https://link.springer.com/article/10.1007/s13246-019-00779-w
Springer Science and Business Media LLC
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