|ORIGINAL ARTICLE (MARFATIA AWARD)
|Year : 2020 | Volume
| Issue : 3 | Page : 273-282
Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study
Sai Krishna Tikka1, Bikesh Kumar Singh2, S Haque Nizamie3, Shobit Garg4, Sunandan Mandal5, Kavita Thakur5, Lokesh Kumar Singh1
1 Department of Psychiatry, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India
2 Department of Bio.Medical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India
3 Department of Psychiatry, Central Institute of Psychiatry, Ranchi, Jharkhand, India
4 Department of Psychiatry, Shri Guru Ram Rai Institute of Medical and Health Sciences, Dehradun, Uttarakhand, India
5 School of Studies in Electronics and Photonics, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
Background: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.
Aims: To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording.
Settings and Design: Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute.
Materials and Methods: Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification.
Statistical Analysis: Mann–Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications.
Results: Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances.
Conclusions: SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the “validity” and reducing the “heterogeneity” in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.
Dr. Sai Krishna Tikka
Department of Psychiatry, All India Institute of Medical Sciences, Raipur - 492 099, Chhattisgarh
Source of Support: None, Conflict of Interest: None
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