Key result
SPWVD-ViT model achieves 87% subject-independent accuracy for classifying schizophrenia using EEG signals.
Why the study?
Combining image generation techniques with effective machine learning algorithms may accelerate schizophrenia treatment and enable early alert systems to prevent disease progression.
Does the SPWVD-ViT model accurately detect schizophrenia from EEG recordings in patients compared to healthy controls?
Does the SPWVD-ViT model accurately detect schizophrenia from EEG recordings in patients compared to healthy controls?
The SPWVD-ViT model demonstrates high accuracy in detecting schizophrenia from EEG recordings, suggesting potential utility as an automated diagnostic tool.
Answers are grounded in this paper and cite their sources.
May support EEG-based AI screening for schizophrenia; hypothesis-generating and requires prospective validation before clinical adoption.
Schizophrenia is a typical neurological disease that affects patients’ mental state, and daily behaviours. Combining image generation techniques with effective machine learning algorithms may accelerate treatment process, and possible early alert systems prevents diseases from reaching out crucial phase. The purpose of current study is to develop an automated EEG based schizophrenia detection with the Vision Transformer (ViT) model using Smoothed Pseudo Wigner Ville Distribution (SPWVD) time-frequency input images. EEG recordings from 35 schizophrenia (sch) and 35 healthy conditions (hc) are analyzed. We have used 5-fold cross validation for evaluation and testing of the method. Classification task is carried out as subject-independent and subject-dependent method. We reached out overall accuracy of 87% for subject-independent and 100% for subject-dependent approach for binary classification. While ViT has ben extensively used in Natural Language Processing (NLP) field, dividing input images within a sequence of embedded image patches via. transformer encoder is a practical way for medical image learning and developing diagnostic tools. SPWVD-ViT model is recommended as a disease detection tool not only for schizophrenia but other neurological symptoms.
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Şeker et al. (2022) studied Schizophrenia (n=70). SPWVD-ViT Model vs. Healthy controls was evaluated on Subject-independent binary classification accuracy. The proposed SPWVD-ViT model achieved an overall accuracy of 87% for subject-independent and 100% for subject-dependent binary classification of schizophrenia using EEG signals.
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