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AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis

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  • Elena-Anca Paraschiv

    (National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania
    Doctoral School of Electronics, Telecommunications & Information Technology, National University of Science and Technology POLITEHNICA, 060042 Bucharest, Romania)

  • Lidia Băjenaru

    (National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania
    Department of Computer Science, Faculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA, 060042 Bucharest, Romania)

  • Cristian Petrache

    (National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania)

  • Ovidiu Bica

    (National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania)

  • Dragoș-Nicolae Nicolau

    (National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania)

Abstract

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients.

Suggested Citation

  • Elena-Anca Paraschiv & Lidia Băjenaru & Cristian Petrache & Ovidiu Bica & Dragoș-Nicolae Nicolau, 2024. "AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis," Future Internet, MDPI, vol. 16(11), pages 1-21, November.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:424-:d:1522232
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    References listed on IDEAS

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    1. Sarah Alami & Laurène Courouve & Guila Lancman & Pierrette Gomis & Gisele Al-Hamoud & Corinne Laurelli & Hélène Pasche & Gilles Chatellier & Grégoire Mercier & François Roubille & Cécile Delval & Isab, 2023. "Organisational Impact of a Remote Patient Monitoring System for Heart Failure Management: The Experience of 29 Cardiology Departments in France," IJERPH, MDPI, vol. 20(5), pages 1-10, February.
    2. Harald Baumeister & Yannik Terhorst & Cora Grässle & Maren Freudenstein & Rüdiger Nübling & David Daniel Ebert, 2020. "Impact of an acceptance facilitating intervention on psychotherapists’ acceptance of blended therapy," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
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