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Applied Sciences
Volume 16
Issue 10
10.3390/app16104697
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods

by
Mariusz Pelc
1,*,
Magda Zolubak
2,
Dariusz Mikolajewski
3,4,
Kamil Adamczewski
1,
Katarzyna Bialas
5,
Rafal Chalupnik
5,
Adrian Luckiewicz
6,
Dawid Krutul
7,
Mateusz Korycinski
7,
Dawid Wolkiewicz
1,
Waldemar Karwowski
8 and
Aleksandra Kawala-Sterniuk
1,*
1
Department of Artificial Intelligence, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
2
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
3
Faculty of Computer Science, Kazimierz Wielki University, 85-064 Bydgoszcz, Poland
4
Neuropsychological Research Unit, 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation, Medical University in Lublin, 20-439 Lublin, Poland
5
Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
6
Faculty of Electrical Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland
7
NASK—National Research Institute, 01-045 Warsaw, Poland
8
Department of Industrial Engineering and Management Systems, University of Central Florida, Orlndo, FL 32816, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2026, 16(10), 4697; https://doi.org/10.3390/app16104697 (registering DOI)
Submission received: 13 February 2026 / Revised: 7 May 2026 / Accepted: 8 May 2026 / Published: 9 May 2026
(This article belongs to the Special Issue Artificial Intelligence Innovations for Smart and Sustainable Healthcare)
Versions Notes

Abstract

Increasing societal awareness of mental health challenges has significantly reduced stigma surrounding psychological disorders, encouraging greater numbers of individuals to seek professional support, which has placed unprecedented pressure on mental health services, with institutions ranging from educational establishments to emergency services implementing systematic screening protocols to identify individuals requiring intervention. However, the growing demand for rapid, accurate diagnosis continues to strain limited professional resources. Our study introduces an innovative machine learning framework for mental disorder detection using electroencephalography (EEG) signals processed through Welch’s power spectral density estimation. Unlike conventional Fast Fourier Transform (FFT) approaches, our method generates refined two-dimensional spectrograms capturing brain wave amplitudes (in dB) alongside precise peak frequency identification. This computationally efficient periodogram variant enables robust feature extraction suitable for real-time diagnostic applications while reducing model training overhead. Preliminary analysis demonstrates the Welch Transform’s superior signal characterization compared to standard FFT periodograms, revealing distinct neurophysiological patterns associated with various mental health conditions. The approach maintains high computational efficiency, supporting potential deployment in clinical screening environments.
Keywords: Welch transform; electroencephalography; mental disorder detection; frequency-domain analysis Welch transform; electroencephalography; mental disorder detection; frequency-domain analysis

Share and Cite

MDPI and ACS Style

Pelc, M.; Zolubak, M.; Mikolajewski, D.; Adamczewski, K.; Bialas, K.; Chalupnik, R.; Luckiewicz, A.; Krutul, D.; Korycinski, M.; Wolkiewicz, D.; et al. Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods. Appl. Sci. 2026, 16, 4697. https://doi.org/10.3390/app16104697

AMA Style

Pelc M, Zolubak M, Mikolajewski D, Adamczewski K, Bialas K, Chalupnik R, Luckiewicz A, Krutul D, Korycinski M, Wolkiewicz D, et al. Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods. Applied Sciences. 2026; 16(10):4697. https://doi.org/10.3390/app16104697

Chicago/Turabian Style

Pelc, Mariusz, Magda Zolubak, Dariusz Mikolajewski, Kamil Adamczewski, Katarzyna Bialas, Rafal Chalupnik, Adrian Luckiewicz, Dawid Krutul, Mateusz Korycinski, Dawid Wolkiewicz, and et al. 2026. "Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods" Applied Sciences 16, no. 10: 4697. https://doi.org/10.3390/app16104697

APA Style

Pelc, M., Zolubak, M., Mikolajewski, D., Adamczewski, K., Bialas, K., Chalupnik, R., Luckiewicz, A., Krutul, D., Korycinski, M., Wolkiewicz, D., Karwowski, W., & Kawala-Sterniuk, A. (2026). Initial Study on Mental Disease Detection System Using Welch Transform and Machine Learning-Based Methods. Applied Sciences, 16(10), 4697. https://doi.org/10.3390/app16104697

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