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Mostafavi M, Ko SB, Shokouhi SB, Ayatollahi A. Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images. Phys Eng Sci Med 2024:10.1007/s13246-024-01420-1. [PMID: 38652347 DOI: 10.1007/s13246-024-01420-1] [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: 08/18/2023] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In fact, individuals with SZ experience a blurred line between fantasy and reality, leading to a lack of awareness about their condition, which can pose significant challenges during the treatment process. Due to the importance of the issue, timely diagnosis of this illness can not only assist patients and their families in managing the condition but also enable early intervention, which may help prevent its advancement. EEG is a widely utilized technique for investigating mental disorders like SZ due to its non-invasive nature, affordability, and wide accessibility. In this study, our main goal is to develop an optimized system that can achieve automatic diagnosis of SZ with minimal input information. To optimize the system, we adopted a strategy of using single-channel EEG signals and integrated knowledge distillation and transfer learning techniques into the model. This approach was designed to improve the performance and efficiency of our proposed method for SZ diagnosis. Additionally, to leverage the pre-trained models effectively, we converted the EEG signals into images using Continuous Wavelet Transform (CWT). This transformation allowed us to harness the capabilities of pre-trained models in the image domain, enabling automatic SZ detection with enhanced efficiency. To achieve a more robust estimate of the model's performance, we employed fivefold cross-validation. The accuracy achieved from the 5-s records of the EEG signal, along with the combination of self-distillation and VGG16 for the P4 channel, is 97.81. This indicates a high level of accuracy in diagnosing SZ using the proposed method.
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Affiliation(s)
- Mohammadreza Mostafavi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Seok-Bum Ko
- Division of Biomedical Engineering, Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada.
| | - Shahriar Baradaran Shokouhi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ahmad Ayatollahi
- Department of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology, Tehran, Iran
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Chatterjee I, Baumgartner L, Cho M. Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning. Front Neurol 2023; 14:1195923. [PMID: 37333009 PMCID: PMC10273207 DOI: 10.3389/fneur.2023.1195923] [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: 04/04/2023] [Accepted: 05/11/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time. Methods In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately. Results Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen. Discussion This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.
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Affiliation(s)
- Indranath Chatterjee
- Department of Computer Engineering, Tongmyong University, Busan, Republic of Korea
- School of Technology, Woxsen University, Telangana, India
| | - Lea Baumgartner
- Department of Computer Engineering, Tongmyong University, Busan, Republic of Korea
- Department of Media, Hochschule der Medien, University of Applied Science, Stuttgart, Germany
| | - Migyung Cho
- Department of Game Engineering, Tongmyong University, Busan, Republic of Korea
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Parija S, Sahani M, Bisoi R, Dash PK. Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification. Pattern Anal Appl 2022. [DOI: 10.1007/s10044-022-01107-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 2022; 146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
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NDCN-Brain: An Extensible Dynamic Functional Brain Network Model. Diagnostics (Basel) 2022; 12:diagnostics12051298. [PMID: 35626453 PMCID: PMC9142118 DOI: 10.3390/diagnostics12051298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/17/2022] [Accepted: 05/23/2022] [Indexed: 11/17/2022] Open
Abstract
As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics (Basel) 2021; 11:393. [PMID: 33669114 PMCID: PMC7996506 DOI: 10.3390/diagnostics11030393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
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Affiliation(s)
- Mahsa Mansourian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Sadaf Khademi
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
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Chatterjee I, Mittal K. A Concise Study of Schizophrenia and Resting-state fMRI data analysis. QEIOS 2020. [DOI: https://doi.org/10.32388/599711.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med 2020; 43:1229-1239. [DOI: 10.1007/s13246-020-00925-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/08/2020] [Indexed: 12/17/2022]
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Impact of ageing on the brain regions of the schizophrenia patients: an fMRI study using evolutionary approach. MULTIMEDIA TOOLS AND APPLICATIONS 2020. [DOI: 10.1007/s11042-020-09183-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Identification of changes in grey matter volume using an evolutionary approach: an MRI study of schizophrenia. MULTIMEDIA SYSTEMS 2020. [DOI: 10.1007/s00530-020-00649-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9142870] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.
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Chatterjee I. Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients. F1000Res 2019; 7:1615. [PMID: 30687497 PMCID: PMC6338245 DOI: 10.12688/f1000research.16405.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/12/2018] [Indexed: 02/05/2023] Open
Abstract
Background: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task. Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls. Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus. Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.
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Chatterjee I, Kumar V, Sharma S, Dhingra D, Rana B, Agarwal M, Kumar N. Identification of brain regions associated with working memory deficit in schizophrenia. F1000Res 2019; 8:124. [PMID: 31069066 PMCID: PMC6480944 DOI: 10.12688/f1000research.17731.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/15/2019] [Indexed: 02/05/2023] Open
Abstract
Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.
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Affiliation(s)
- Indranath Chatterjee
- Department of Computer Science, University of Delhi, Delhi, DELHI, 110007, India
| | - Virendra Kumar
- Department of NMR and MRI Facility, All India Institute of Medical Sciences, Delhi, DELHI, 110029, India
| | - Sahil Sharma
- Department of Computer Science, University of Delhi, Delhi, DELHI, 110007, India
| | - Divyanshi Dhingra
- Department of Computer Science, University of Delhi, Delhi, DELHI, 110007, India
| | - Bharti Rana
- Department of Computer Science, Hans Raj College, University of Delhi, Delhi, DELHI, 110007, India
| | - Manoj Agarwal
- Department of Computer Science, Hans Raj College, University of Delhi, Delhi, DELHI, 110007, India
| | - Naveen Kumar
- Department of Computer Science, University of Delhi, Delhi, DELHI, 110007, India
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