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

Elena-Anca Paraschiv (), Lidia Băjenaru (), Cristian Petrache, Ovidiu Bica and Dragoș-Nicolae Nicolau
Additional contact information
Elena-Anca Paraschiv: National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, Romania
Lidia Băjenaru: National Institute for Research and Development in Informatics—ICI Bucharest, 011455 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

Future Internet, 2024, vol. 16, issue 11, 1-21

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.

Keywords: schizophrenia; EEG; deep learning; CNN-BiLSTM; transfer entropy; mental health monitoring (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/11/424/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/11/424/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:11:p:424-:d:1522232

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().