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Rao AP, Ranjan R, Sahana BC, Kumar GP. SchizoLMNet: a modified lightweight MobileNetV2- architecture for automated schizophrenia detection using EEG-derived spectrograms. Phys Eng Sci Med 2025:10.1007/s13246-024-01512-y. [PMID: 39760847 DOI: 10.1007/s13246-024-01512-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 12/20/2024] [Indexed: 01/07/2025]
Abstract
Schizophrenia (SZ) is a chronic neuropsychiatric disorder characterized by disturbances in cognitive, perceptual, social, emotional, and behavioral functions. The conventional SZ diagnosis relies on subjective assessments of individuals by psychiatrists, which can result in bias, prolonged procedures, and potentially false diagnoses. This emphasizes the crucial need for early detection and treatment of SZ to provide timely support and minimize long-term impacts. Utilizing the ability of electroencephalogram (EEG) signals to capture brain activity dynamics, this article introduces a novel lightweight modified MobileNetV2- architecture (SchizoLMNet) for efficiently diagnosing SZ using spectrogram images derived from selected EEG channel data. The proposed methodology involves preprocessing of raw EEG data of 81 subjects collected from Kaggle data repository. Short-time Fourier transform (STFT) is applied to transform pre-processed EEG signals into spectrogram images followed by data augmentation. Further, the generated images are subjected to deep learning (DL) models to perform the binary classification task. Utilizing the proposed model, it achieved accuracies of 98.17%, 97.03%, and 95.55% for SZ versus healthy classification in hold-out, subject independent testing, and subject-dependent testing respectively. The SchizoLMNet model demonstrates superior performance compared to various pretrained DL models and state-of-the-art techniques. The proposed framework will be further translated into real-time clinical settings through a mobile edge computing device. This innovative approach will serve as a bridge between medical staff and patients, facilitating intelligent communication and assisting in effective SZ management.
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Affiliation(s)
- A Prabhakara Rao
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, 500043, India
| | - Rakesh Ranjan
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, 800005, India.
- School of Computer Science, UPES, Dehradun, Uttarakhand, 248007, India.
| | - Bikash Chandra Sahana
- Department of Electronics and Communication Engineering, National Institute of Technology Patna, Bihar, 800005, India
| | - G Prasanna Kumar
- Department of Electronics and Communication Engineering, Vishnu Institute of Technology, Bhimavaram, Andhra Pradesh, 534202, India
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Zhang T, Zhao X, Yeo BT, Huo X, Eickhoff SB, Chen J. Leveraging Stacked Classifiers for Multi-task Executive Function in Schizophrenia Yields Diagnostic and Prognostic Insights. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.12.05.24318587. [PMID: 39677485 PMCID: PMC11643294 DOI: 10.1101/2024.12.05.24318587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
Cognitive impairment is a central characteristic of schizophrenia. Executive functioning (EF) impairments are often seen in mental disorders, particularly schizophrenia, where they relate to adverse outcomes. As a heterogeneous construct, how specifically each dimension of EF to characterize the diagnostic and prognostic aspects of schizophrenia remains opaque. We used classification models with a stacking approach on systematically measured EFs to discriminate 195 patients with schizophrenia from healthy individuals. Baseline EF measurements were moreover employed to predict symptomatically remitted or non-remitted prognostic subgroups. EF feature importance was determined at the group-level and the ensuing individual importance scores were associated with four symptom dimensions. EF assessments of inhibitory control (interference and response inhibitions), followed by working memory, evidently predicted schizophrenia diagnosis (area under the curve [AUC]=0.87) and remission status (AUC=0.81). The models highlighted the importance of interference inhibition or working memory updating in accurately identifying individuals with schizophrenia or those in remission. These identified patients had high-level negative symptoms at baseline and those who remitted showed milder cognitive symptoms at follow-up, without differences in baseline EF or symptom severity compared to non-remitted patients. Our work indicates that impairments in specific EF dimensions in schizophrenia are differentially linked to individual symptom-load and prognostic outcomes. Thus, assessments and models based on EF may be a promising tool that can aid in the clinical evaluation of this disorder.
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Affiliation(s)
- Tongyi Zhang
- School of Psychology, Northwest Normal University, Lanzhou, China
| | - Xin Zhao
- School of Psychology, Northwest Normal University, Lanzhou, China
| | - B.T. Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore
- Integrative Sciences & Engineering Programme, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Xiaoning Huo
- The Third People’s Hospital of Lanzhou, Lanzhou, China
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Center for Brain Health and Brain Technology, Global Institute of Future Technology, Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University 800 Dongchuan Road, Shanghai, China 200240
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
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Wang S, Tang H, Himeno R, Solé-Casals J, Caiafa CF, Han S, Aoki S, Sun Z. Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108419. [PMID: 39293231 DOI: 10.1016/j.cmpb.2024.108419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 09/01/2024] [Accepted: 09/08/2024] [Indexed: 09/20/2024]
Abstract
BACKGROUND AND OBJECTIVE The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions. METHODS This paper proposes an evolutionary algorithm (EA) based graph neural architecture search (GNAS) method. EA-GNAS has the ability to search for high-performance graph neural networks for schizophrenia spectrum disorder prediction. Moreover, we adopt GNNExplainer to investigate the explainability of the acquired architectures, ensuring that the model's predictions are both accurate and comprehensible. RESULTS The results suggest that the graph neural network model, derived using genetic algorithm search, outperforms under five-fold cross-validation, achieving a fitness of 0.1850. Relative to conventional machine learning and other deep learning approaches, the proposed method yields superior accuracy, F1 score, and AUC values of 0.8246, 0.8438, and 0.8258, respectively. CONCLUSION Based on a multi-site dataset from schizophrenia spectrum disorder patients, the findings reveal an enhancement over prior methods, advancing our comprehension of brain function and potentially offering a biomarker for diagnosing schizophrenia spectrum disorder.
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Affiliation(s)
- Shurun Wang
- School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan.
| | - Hao Tang
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Industrial Automation Engineering Technology Research Center of Anhui Province, Hefei, 230009, China
| | - Ryutaro Himeno
- Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Spain; Department of Psychiatry, University of Cambridge, Cambridge, CB2 3EB, United Kingdom
| | - Cesar F Caiafa
- Instituto Argentino de Radioastronomía-CONICET CCT La Plata/CIC-PBA/UNLP, V. Elisa, 1894, Argentina
| | - Shuning Han
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, 08500, Spain; Image Processing Research Group, RIKEN Center for Advanced Photonics, RIKEN, Wako-Shi, Saitama, 351-0198, Japan
| | - Shigeki Aoki
- Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan
| | - Zhe Sun
- Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan.
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Rahul J, Sharma D, Sharma LD, Nanda U, Sarkar AK. A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning. Front Hum Neurosci 2024; 18:1347082. [PMID: 38419961 PMCID: PMC10899326 DOI: 10.3389/fnhum.2024.1347082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.
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Affiliation(s)
- Jagdeep Rahul
- Department of Electronics and Communication Engineering, Rajiv Gandhi University, Arunachal Pradesh, India
| | - Diksha Sharma
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
| | - Lakhan Dev Sharma
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Umakanta Nanda
- School of Electronics Engineering, VIT-AP University, Amrawati, India
| | - Achintya Kumar Sarkar
- Department of Electronics and Communication, Indian Institute of Information Technology, Sri City, India
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Sharma M, Lodhi H, Yadav R, Elphick H, Acharya UR. Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 235:107471. [PMID: 37037163 DOI: 10.1016/j.cmpb.2023.107471] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Sleep quality is associated with wellness, and its assessment can help diagnose several disorders and diseases. Sleep analysis is commonly performed based on self-rating indices, sleep duration, environmental factors, physiologically and polysomnographic-derived parameters, and the occurrence of disorders. However, the correlation that has been observed between the subjective assessment and objective measurements of sleep quality is small. Recently, a few automated systems have been suugested to measure sleep quality to address this challenge. Sleep quality can be assessed by evaluating macrostructure-based sleep analysis via the examination of sleep cycles, namely Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) with N1, N2, and N3 stages. However, macrostructure sleep analysis does not consider transitory phenomena like K-complexes and transient fluctuations, which are indispensable in diagnosing various sleep disorders. The CAP, part of the microstructure of sleep, may offer a more precise and relevant examination of sleep and can be considered one of the candidates to measure sleep quality and identify sleep disorders such as insomnia and apnea. CAP is characterized by very subtle changes in the brain's electroencephalogram (EEG) signals that occur during the NREM stage of sleep. The variations among these patterns in healthy subjects and subjects with sleep disorders can be used to identify sleep disorders. Studying CAP is highly arduous for human experts; thus, developing automated systems for assessing CAP is gaining momentum. Developing new techniques for automated CAP detection installed in clinical setups is essential. This paper aims to analyze the algorithms and methods presented in the literature for the automatic assessment of CAP and the development of CAP-based sleep markers that may enhance sleep quality assessment, helping diagnose sleep disorders. METHODS This literature survey examined the automated assessment of CAP and related parameters. We have reviewed 34 research articles, including fourteen ML, nine DL, and ten based on some other techniques. RESULTS The review includes various algorithms, databases, features, classifiers, and classification performances and their comparisons, advantages, and limitations of automated systems for CAP assessment. CONCLUSION A detailed description of state-of-the-art research findings on automated CAP assessment and associated challenges has been presented. Also, the research gaps have been identified based on our review. Further, future research directions are suggested for sleep quality assessment using CAP.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Harsh Lodhi
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Rishita Yadav
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | | | - U Rajendra Acharya
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Department of Biomedical Engineering, School of Science and Technology, Singapore.
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