Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition ta...
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MDPI AG
2025-07-01
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| author | Alka Jalan Deepti Mishra Marisha Manjari Gupta |
| author_facet | Alka Jalan Deepti Mishra Marisha Manjari Gupta |
| author_sort | Alka Jalan |
| collection | DOAJ |
| description | Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making. |
| format | Article |
| id | doaj-art-9b34db5fab24493f8b768a0bdfea2f75 |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Biomimetics |
| spelling | doaj-art-9b34db5fab24493f8b768a0bdfea2f752025-08-20T02:45:33ZengMDPI AGBiomimetics2313-76732025-07-0110744910.3390/biomimetics10070449Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep LearningAlka Jalan0Deepti Mishra1Marisha2Manjari Gupta3Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, IndiaDepartment of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, NorwayDepartment of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, IndiaDepartment of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, IndiaDiagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making.https://www.mdpi.com/2313-7673/10/7/449schizophreniaMarkov Transition FieldVGG-16electroencephalogramdeep learningexplainability |
| spellingShingle | Alka Jalan Deepti Mishra Marisha Manjari Gupta Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning Biomimetics schizophrenia Markov Transition Field VGG-16 electroencephalogram deep learning explainability |
| title | Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning |
| title_full | Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning |
| title_fullStr | Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning |
| title_full_unstemmed | Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning |
| title_short | Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning |
| title_sort | diagnosis of schizophrenia using feature extraction from eeg signals based on markov transition fields and deep learning |
| topic | schizophrenia Markov Transition Field VGG-16 electroencephalogram deep learning explainability |
| url | https://www.mdpi.com/2313-7673/10/7/449 |
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