Discrete mathematical modelling for enhancing mental illness detection

Dayal, Mohit; Mahajan, Aparna N.; Khari, Manju

doi:10.47974/JDMSC-2341

Open Access Research Article

Discrete mathematical modelling for enhancing mental illness detection

^*Mohit DayalCorresponding authormohitdayal.md@gmail.comDepartment of Computer Science & EngineeringMaharaja Agrasen UniversityBaddi, Himachal Pradesh, 174103, IndiaView full profile → , Aparna N. Mahajanaparnanmahajan@yahoo.inDepartment of Computer Science & EngineeringMaharaja Agrasen UniversityBaddi, Himachal Pradesh, 174103, IndiaView full profile → , Manju Kharimanjukhari@yahoo.co.inSchool of Computer & System SciencesJawaharlal Nehru UniversityNew Delhi, 110067, IndiaView full profile →

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pp. 2573–2581Vol. 28Issue 6September 2025DOI: 10.47974/JDMSC-2341 Crossmark XML

Received:: 13 Mar 2024
Published Online:: 09 Jul 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2341
Pages:: 2573–2581

Abstract

In this paper a machine learning approach is used for diagnosis of mental illness such as Schizophrenia (SCZ) using Electroencephalogram (EEG) signals. EEG signals helps identification of normal and abnormal brain signals. In this approach, the methodology is divided in three major steps: data collection and pre-processing, feature extraction, and classification. The main focus of the work is feature extraction. For this multi-channel pattern correlation and feature merging (MCPCFM) is proposed that identifies and combines the linear and non-linear features using CNN model. Then data augmentation is also applied to handle data imbalance issue for training random forest classifier. This framework aims to efficiently represent EEG signals and reduce feature dimensionality issue to improve mental illness classification performance. The result analysis shows 90% of accuracy for detection. As compared to state-of-art models the proposed model outperforms best.

Keywords

Mental health Schizophrenia detection EEG Feature engineering CNN model

Subject Classifications

Primary 424C0Secondary 94A12

References

[1] Saloni Dattani, Hannah Ritchie, and Max Roser. “Mental Health.” Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/mental-health (2021).
[2] Yuyang Fan, Rui Yu, Jie Li, Jing Zhu, and Xiang Li. “EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal feature.” 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE (2020).
[3] Kritiprasanna Das and Ram Bilas Pachori. “Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals.” Biomedical Signal Processing and Control, vol. 67, Article 102525 (2021).
[4] Ashima Tyagi, Vibhav Prakash Singh, and Manoj Madhava Gore. “Towards artificial intelligence in mental health: A comprehensive survey on the detection of schizophrenia.” Multimedia Tools and Applications, vol. 82, no. 13, pp. 20343–20405 (2023).
[5] Syed Yasin, S. A. Hussain, S. Aslan, I. Raza, M. Muzammel, and A. Othmani. “EEG based Major Depressive Disorder and Bipolar Disorder detection using Neural Networks: A review.” Computer Methods and Programs in Biomedicine, vol. 202, Article 106007 (2021).
[6] Syed Siuly, Smith K. Khare, Varun Bajaj, Hui Wang, and Yi Zhang. “A computerized method for automatic detection of schizophrenia using EEG signals.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 11, pp. 2390–2400 (2020).
[7] Ahmad Shalbaf, Sara Bagherzadeh, and Arash Maghsoudi. “Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals.” Physical and Engineering Sciences in Medicine, vol. 43, pp. 1229–1239 (2020).
[8] Smith K. Khare, Varun Bajaj, and U. Rajendra Acharya. “SPWVD-CNN for automated detection of schizophrenia patients using EEG signals.” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9 (2021).
[9] Smith K. Khare and Varun Bajaj. “A hybrid decision support system for automatic detection of schizophrenia using EEG signals.” Computers in Biology and Medicine, vol. 141, Article 105028 (2022).
[10] Ankur Seal, Rakesh Bajpai, Jaideep Agnihotri, Ahmed Yazidi, Enrique Herrera-Viedma, and Ondrej Krejcar. “DeprNet: A deep convolution neural network framework for detecting depression using EEG.” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–13 (2021).
[11] Geetanjali Sharma, Abhishek Parashar, and Amit M. Joshi. “DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression.” Biomedical Signal Processing and Control, vol. 66, Article 102393 (2021).
[12] Aditi Sakalle. “A modified LSTM framework for analyzing COVID-19 effect on emotion and mental health during pandemic using EEG signals.” Journal of Healthcare Engineering (2022).
[13] E. Aydemir, T. Tuncer, S. Dogan, R. Gururajan, and U. Rajendra Acharya. “Automated major depressive disorder detection using melamine pattern with EEG signals.” Applied Intelligence, vol. 51, no. 9, pp. 6449–6466 (2021).
[14] H. W. Loh, C. P. Ooi, E. Aydemir, T. Tuncer, S. Dogan, and U. Rajendra Acharya. “Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals.” Expert Systems, vol. 39, no. 3, e12773 (2022).
[15] Sang Min Park, Byoung Oh Jeong, Do Yoon Oh, Chang Hyun Choi, Hyun Young Jung, Jin Young Lee, and Jin Seok Choi. “Identification of major psychiatric disorders from resting-state electroencephalography using a machine learning approach.” Frontiers in Psychiatry, vol. 12, Article 707581 (2021).
[16] Gokhan Tasci, M. Volkan Gun, Tugce Keles, Burcu Tasci, Pranesh D. Barua, Ilke Tasci, and U. Rajendra Acharya. “QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals.” Chaos, Solitons & Fractals, vol. 172, Article 113472 (2023).
[17] Dataset, Available at: https://www.kaggle.com/datasets/broach/button-tone-sz.
[18] Keshav Upreti, Shih-Lin Peng, Prashant R. Kshirsagar, Pradeep Chakrabarti, Hamad A. Al-Alshaikh, Ajay Kumar Sharma, and Ramesh C. Poonia. “A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks.” Journal of Discrete Mathematical Sciences and Cryptography, vol. 26, no. 6, pp. 1819–1834 (2023).
[19] Keshav Upreti, Sandeep Arora, Ajay Kumar Sharma, Anil Kumar Pandey, K. K. Sharma, and Mudit Dayal. “Wave height forecasting over ocean of things based on machine learning techniques: An application for ocean renewable energy generation.” IEEE Journal of Oceanic Engineering, vol. 49, no. 2, pp. 430–445 (2023).
[20] Ashish B. Kanwade, M. P. Sardey, S. A. Panwar, M. P. Gajare, M. N. Chaudhari, and Keshav Upreti. “Combined weighted feature extraction and deep learning approach for chronic obstructive pulmonary disease classification using electromyography.” International Journal of Information Technology, vol. 16, no. 3, 2024, pp. 1485–1494 (2024).

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