AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis
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 warning...
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2024-11-01
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| author | Elena-Anca Paraschiv Lidia Băjenaru Cristian Petrache Ovidiu Bica Dragoș-Nicolae Nicolau |
| author_facet | Elena-Anca Paraschiv Lidia Băjenaru Cristian Petrache Ovidiu Bica Dragoș-Nicolae Nicolau |
| author_sort | Elena-Anca Paraschiv |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-25baf5f2e2f549238b55ce5b637bef62 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Future Internet |
| spelling | doaj-art-25baf5f2e2f549238b55ce5b637bef622024-11-26T18:05:17ZengMDPI AGFuture Internet1999-59032024-11-01161142410.3390/fi16110424AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG AnalysisElena-Anca Paraschiv0Lidia Băjenaru1Cristian Petrache2Ovidiu Bica3Dragoș-Nicolae Nicolau4National Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, RomaniaNational Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, RomaniaNational Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, RomaniaNational Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, RomaniaNational Institute for Research and Development in Informatics—ICI Bucharest, 011455 Bucharest, RomaniaSchizophrenia 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.https://www.mdpi.com/1999-5903/16/11/424schizophreniaEEGdeep learningCNN-BiLSTMtransfer entropymental health monitoring |
| spellingShingle | Elena-Anca Paraschiv Lidia Băjenaru Cristian Petrache Ovidiu Bica Dragoș-Nicolae Nicolau AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis Future Internet schizophrenia EEG deep learning CNN-BiLSTM transfer entropy mental health monitoring |
| title | AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis |
| title_full | AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis |
| title_fullStr | AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis |
| title_full_unstemmed | AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis |
| title_short | AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis |
| title_sort | ai driven neuro monitoring advancing schizophrenia detection and management through deep learning and eeg analysis |
| topic | schizophrenia EEG deep learning CNN-BiLSTM transfer entropy mental health monitoring |
| url | https://www.mdpi.com/1999-5903/16/11/424 |
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