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Depressive Disorder Prediction Using Machine Learning-Based Electroencephalographic Signal

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Artificial Intelligence for Smart Healthcare

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Machine learning is an effective method to analyze enormous information sets precisely, particularly those with recognized designs, which have also proven to be extremely important for the determination of EEG signals because of their reduced bias frequency and high design sensitivity. Among the most severe mental disorders is a mental illness, which is even worse when it leads to suicide. It is critical to diagnose depression in its early stages. Electroencephalographic (EEG) signals are acquired from a publicly accessible database and analyzed in MATLAB in this paper; it may also lead to various abnormalities such as sleep problems and alcohol addiction. Using the classifier tools included in it can help categorize topics with disorders. The parameters are taken from frequency bands (Alpha, Delta, Theta, Beta).

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, St. Martin’s Engineering College, Secunderabad, Telangana, India

    Govinda Rajulu Ganiga

  2. Department of Electronics and Communication Engineering, Podhigai College of Engineering and Technology, Tirupattur, Tamil Nadu, India

    Kalvikkarasi Subramani

  3. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Dilip Kumar Sharma

  4. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India

    Sudhakar Sengan

  5. Department of Information Technology, Muthayammal Engineering College, Rasipuram, Tamil Nadu, India

    Kalaiyarasi Anbalagan

  6. Department of Computer Science and Engineering, P.S.R Engineering College, Sivakasi, Tamil Nadu, India

    Priyadarsini Seenivasan

Authors
  1. Govinda Rajulu Ganiga
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  2. Kalvikkarasi Subramani
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  3. Dilip Kumar Sharma
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  4. Sudhakar Sengan
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  5. Kalaiyarasi Anbalagan
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  6. Priyadarsini Seenivasan
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Jamia Hamdard, New Delhi, India

    Parul Agarwal

  2. Delhi Skill and Entrepreneurship University, Dwarka, Delhi, India

    Kavita Khanna

  3. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt

    Ahmed A Elngar

  4. Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq

    Ahmed J. Obaid

  5. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

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Ganiga, G.R., Subramani, K., Sharma, D.K., Sengan, S., Anbalagan, K., Seenivasan, P. (2023). Depressive Disorder Prediction Using Machine Learning-Based Electroencephalographic Signal. In: Agarwal, P., Khanna, K., Elngar, A.A., Obaid, A.J., Polkowski, Z. (eds) Artificial Intelligence for Smart Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-23602-0_11

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  • DOI: https://doi.org/10.1007/978-3-031-23602-0_11

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  • Online ISBN: 978-3-031-23602-0

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