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Jeng AC, Sibley IJ, Bale TL. A global perspective on AI innovation and effective use in the research lab. Neuroscience 2024; 560:106-108. [PMID: 39307414 DOI: 10.1016/j.neuroscience.2024.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 09/17/2024] [Indexed: 09/28/2024]
Affiliation(s)
- Alyssa C Jeng
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Isabelle J Sibley
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Tracy L Bale
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
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2
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Tyagi A, Singh VP, Gore MM. Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning. Neuroinformatics 2024:10.1007/s12021-024-09684-4. [PMID: 39298101 DOI: 10.1007/s12021-024-09684-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2024] [Indexed: 09/21/2024]
Abstract
Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinking, perception, and behavior. Early identification of schizophrenia can help prevent potential complications and facilitate effective treatment and management of the condition. This paper proposes a computer aided diagnosis system for the early detection of schizophrenia using 19-channel Electroencephalography (EEG) signals from 28 subjects, leveraging statistical moments of Mel-frequency Cepstral Coefficients (MFCC) and ensemble learning. Initially, the EEG signals are passed through a high-pass filter to mitigate noise and remove extraneous data. The feature extraction technique is then employed to extract MFC coefficients from the filtered EEG signals. The dimensionality of these coefficients is reduced by computing their statistical moments, which include the mean, standard deviation, skewness, kurtosis, and energy. Subsequently, the Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is applied to identify pertinent features from the statistical moments of the MFC coefficients. These SVM-RFE-based selected features serve as input for three base classifiers: Support Vector Machine, k-Nearest Neighbors, and Logistic Regression. Additionally, an ensemble learning approach, which combines the predictions of the three classifiers through majority voting, is introduced to enhance schizophrenia detection performance and generalize the results of the proposed approach. The study's findings demonstrate that the ensemble model, combined with SVM-RFE-based selected statistical moments of MFCC, achieves encouraging detection performance, highlighting the potential of machine learning techniques in advancing the diagnostic process of schizophrenia.
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Affiliation(s)
- Ashima Tyagi
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India.
| | - Vibhav Prakash Singh
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India
| | - Manoj Madhava Gore
- Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India
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Al Fahoum A, Zyout A. Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG-Based Schizophrenia Detection. Int J Neural Syst 2024; 34:2450046. [PMID: 39010724 DOI: 10.1142/s0129065724500461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm's efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm's robustness. The performance metrics derived from these tests - accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa - indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system's accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.
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Affiliation(s)
- Amjed Al Fahoum
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
| | - Ala'a Zyout
- Biomedical Systems and Informatics Engineering Department, Yarmouk University, Irbid 21163, Jordan
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Alazzawı A, Aljumaili S, Duru AD, Uçan ON, Bayat O, Coelho PJ, Pires IM. Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning. PeerJ Comput Sci 2024; 10:e2170. [PMID: 39314693 PMCID: PMC11419632 DOI: 10.7717/peerj-cs.2170] [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: 03/04/2024] [Accepted: 06/11/2024] [Indexed: 09/25/2024]
Abstract
Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.
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Affiliation(s)
- Athar Alazzawı
- Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbaş University, Istanbul, Turkey
| | - Saif Aljumaili
- Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbaş University, Istanbul, Turkey
| | - Adil Deniz Duru
- Neuroscience and Psychology Research in Sports Lab, Faculty of Sport Science, Marmara University Istanbul, Istanbul, Turkey
| | - Osman Nuri Uçan
- Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbaş University, Istanbul, Turkey
| | - Oğuz Bayat
- Electrical and Computer Engineering, School of Engineering and Natural Sciences, Altinbaş University, Istanbul, Turkey
| | - Paulo Jorge Coelho
- Polytechnic Institute of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
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Xu X, Zhu G, Li B, Lin P, Li X, Wang Z. Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network. Biomed Eng Online 2024; 23:55. [PMID: 38886737 PMCID: PMC11181588 DOI: 10.1186/s12938-024-01250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.
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Affiliation(s)
- Xinyi Xu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu Mental Health Center, Shanghai, China
| | - Ping Lin
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Shanghai Yangpu Mental Health Center, Shanghai, China.
| | - Zhen Wang
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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Scheffer M, Bockting CL, Borsboom D, Cools R, Delecroix C, Hartmann JA, Kendler KS, van de Leemput I, van der Maas HLJ, van Nes E, Mattson M, McGorry PD, Nelson B. A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review. JAMA Psychiatry 2024; 81:624-630. [PMID: 38568618 DOI: 10.1001/jamapsychiatry.2024.0228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Importance Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.
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Metin B, Uyulan Ç, Ergüzel TT, Farhad S, Çifçi E, Türk Ö, Tarhan N. The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data. Clin EEG Neurosci 2024; 55:167-175. [PMID: 36341750 DOI: 10.1177/15500594221137234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.
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Affiliation(s)
- Barış Metin
- Medical Faculty, Neurology Department, Uskudar University, Istanbul, Turkey
| | - Çağlar Uyulan
- Department of Mechanical Engineering, Katip Çelebi University, İzmir, Turkey
| | - Türker Tekin Ergüzel
- Faculty of Engineering and Natural Sciences, Department of Software Engineering, Uskudar University, Istanbul, Turkey
| | - Shams Farhad
- Department of Neuroscience, Uskudar University, Istanbul, Turkey
| | - Elvan Çifçi
- Department of Psychiatry, Uskudar University, Istanbul, Turkey
| | - Ömer Türk
- Department of Computer Technologies, Artuklu University, Mardin, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Uskudar University, Istanbul, Turkey
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Zhou R, Miller JP, Gordon M, Kass M, Lin M, Peng Y, Li F, Feng J, Liu L. Deep learning models to predict primary open-angle glaucoma. Stat (Int Stat Inst) 2024; 13:e649. [PMID: 39220673 PMCID: PMC11364364 DOI: 10.1002/sta4.649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/17/2023] [Indexed: 09/04/2024]
Abstract
Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).
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Affiliation(s)
- Ruiwen Zhou
- Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA
| | - J. Philip Miller
- Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA
| | - Mae Gordon
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Michael Kass
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, USA
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York City, New York, USA
| | - Fuhai Li
- Institute for Informatics (I2), Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
- Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Jiarui Feng
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri, USA
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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Parsa M, Rad HY, Vaezi H, Hossein-Zadeh GA, Setarehdan SK, Rostami R, Rostami H, Vahabie AH. EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107683. [PMID: 37406421 DOI: 10.1016/j.cmpb.2023.107683] [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: 06/05/2022] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 07/07/2023]
Abstract
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.
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Affiliation(s)
- Mohsen Parsa
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Habib Yousefi Rad
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Hadi Vaezi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Reza Rostami
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran
| | - Hana Rostami
- ACNC, Atieh Clinical Neuroscience Center, Valiasr St., P.O. Box 19697-13663, Tehran, Iran
| | - Abdol-Hossein Vahabie
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395/515, Tehran, Iran; Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran.
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11
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Gashkarimov VR, Sultanova RI, Efremov IS, Asadullin AR. Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review. CONSORTIUM PSYCHIATRICUM 2023; 4:43-53. [PMID: 38249535 PMCID: PMC10795943 DOI: 10.17816/cp11030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/07/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patients quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness. AIM This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features. METHODS The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: machine learning, deep learning, schizophrenia, neural network, predictors, artificial intelligence, diagnostics, suicide, depressive, insomnia, and cognitive. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data. RESULTS Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time. CONCLUSION Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia.
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Affiliation(s)
| | - Renata I Sultanova
- Moscow Research and Clinical Center for Neuropsychiatry of Moscow Healthcare Department
| | - Ilya S Efremov
- Bashkir State Medical University
- V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology
| | - Azat R Asadullin
- Bashkir State Medical University
- V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology
- Republican Clinical Psychotherapeutic Center
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12
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Perellón-Alfonso R, Oblak A, Kuclar M, Škrlj B, Pileckyte I, Škodlar B, Pregelj P, Abellaneda-Pérez K, Bartrés-Faz D, Repovš G, Bon J. Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia. Front Psychiatry 2023; 14:1205119. [PMID: 37817830 PMCID: PMC10560761 DOI: 10.3389/fpsyt.2023.1205119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Introduction Patients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm. Methods We tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM. Results The DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs. Discussion These results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.
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Affiliation(s)
- Ruben Perellón-Alfonso
- Faculty of Medicine and Health Sciences, and Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Aleš Oblak
- University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
| | - Matija Kuclar
- Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Blaž Škrlj
- Jožef Stefan Institute, Ljubljana, Slovenia
| | - Indre Pileckyte
- Center for Brain and Cognition, Pompeu Fabra University, Barcelona, Spain
| | - Borut Škodlar
- University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
- Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Peter Pregelj
- University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
- Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Kilian Abellaneda-Pérez
- Faculty of Medicine and Health Sciences, and Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, Barcelona, Spain
| | - David Bartrés-Faz
- Faculty of Medicine and Health Sciences, and Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Jurij Bon
- University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
- Department of Psychiatry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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13
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Kose MR, Ahirwal MK, Atulkar M. Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal. Phys Eng Sci Med 2023; 46:1055-1070. [PMID: 37222953 DOI: 10.1007/s13246-023-01273-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/02/2023] [Indexed: 05/25/2023]
Abstract
A brain connectivity network (BCN) is an advanced approach to examining brain functionality in various conditions. However, the predictability of the BCN is affected by the connectivity measure used for the network construction. Various connectivity measures available in the literature differ according to the domain of their working data. The application of random connectivity measures might result in an inefficient BCN that ultimately hampers its predictability. Therefore, selecting an appropriate functional connectivity metric is crucial in clinical as well as cognitive neuroscience. In parallel to this, an effective network identifier plays a vital role in distinguishing different brain states. Hence, the objective of this paper is two-fold, which includes identifying suitable connectivity measures and proposing an efficient network identifier. For this, the weighted BCN (WBCN) is constructed using multiple connectivity measures like correlation coefficient (r), coherence (COH), phase-locking value (PLV), and mutual information (MI) from electroencephalogram (EEG) signals. The most recent technique for feature extraction, i.e., weighted ordinal connections, has been applied to EEG-based BCN. EEG signals data has been taken from the schizophrenia disease database. Further, several classification algorithms such as k-nearest neighbours (KNN), support vector machine (SVM) with linear, radial basis function and polynomial kernels, random forest (RF), and 1D convolutional neural network (CNN1D) are used to classify the brain states based on extracted features. In classification, 90% accuracy is achieved by the CNN1D classifier with WBCN based on the coherence connectivity measure. The study also provides a structural analysis of the BCN.
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Affiliation(s)
- Mangesh R Kose
- Department of Computer Application, NIT, Raipur, 492010, CG, India.
| | - Mitul K Ahirwal
- Department of Computer Science and Engineering, MANIT, Bhopal, 462003, MP, India
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14
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Tranter MM, Aggarwal S, Young JW, Dillon DG, Barnes SA. Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification. Neuropsychopharmacology 2023; 48:1377-1385. [PMID: 36509858 PMCID: PMC10354061 DOI: 10.1038/s41386-022-01514-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/21/2022] [Accepted: 11/26/2022] [Indexed: 12/14/2022]
Abstract
The ability to appropriately update the value of a given action is a critical component of flexible decision making. Several psychiatric disorders, including schizophrenia, are associated with impairments in flexible decision making that can be evaluated using the probabilistic reversal learning (PRL) task. The PRL task has been reverse-translated for use in rodents. Disrupting glutamate neurotransmission during early postnatal neurodevelopment in rodents has induced behavioral, cognitive, and neuropathophysiological abnormalities relevant to schizophrenia. Here, we tested the hypothesis that using the NMDA receptor antagonist phencyclidine (PCP) to disrupt postnatal glutamatergic transmission in rats would lead to impaired decision making in the PRL. Consistent with this hypothesis, compared to controls the postnatal PCP-treated rats completed fewer reversals and exhibited disruptions in reward and punishment sensitivity (i.e., win-stay and lose-shift responding, respectively). Moreover, computational analysis of behavior revealed that postnatal PCP-treatment resulted in a pronounced impairment in the learning rate throughout PRL testing. Finally, a deep neural network (DNN) trained on the rodent behavior could accurately predict the treatment group of subjects. These data demonstrate that disrupting early postnatal glutamatergic neurotransmission impairs flexible decision making and provides evidence that DNNs can be trained on behavioral datasets to accurately predict the treatment group of new subjects, highlighting the potential for DNNs to aid in the diagnosis of schizophrenia.
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Affiliation(s)
- Michael M Tranter
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Mental Health, VA San Diego Healthcare System, La Jolla, CA, 92093, USA
| | - Samarth Aggarwal
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jared W Young
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Mental Health, VA San Diego Healthcare System, La Jolla, CA, 92093, USA
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Samuel A Barnes
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Mental Health, VA San Diego Healthcare System, La Jolla, CA, 92093, USA.
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15
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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16
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Roy B, Malviya L, Kumar R, Mal S, Kumar A, Bhowmik T, Hu JW. Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals. Diagnostics (Basel) 2023; 13:diagnostics13111936. [PMID: 37296788 DOI: 10.3390/diagnostics13111936] [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: 04/16/2023] [Revised: 05/14/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stress has an impact, not only on a person's physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.
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Affiliation(s)
- Bishwajit Roy
- Department of Computer Science Engineering-AI & ML, Siliguri Institute of Technology, Siliguri 734009, India
| | - Lokesh Malviya
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Radhikesh Kumar
- Department of Computer Science and Engineering, National Institute of Technology, Patna 800001, India
| | - Sandip Mal
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Amrendra Kumar
- Department of Civil Engineering, Roorkee Institute of Technology, Roorkee 247667, India
| | - Tanmay Bhowmik
- Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, India
| | - Jong Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22022, Republic of Korea
- Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22022, Republic of Korea
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17
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Baygin M, Barua PD, Chakraborty S, Tuncer I, Dogan S, Palmer E, Tuncer T, Kamath AP, Ciaccio EJ, Acharya UR. CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals. Physiol Meas 2023; 44. [PMID: 36599170 DOI: 10.1088/1361-6579/acb03c] [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: 08/18/2022] [Accepted: 01/04/2023] [Indexed: 01/05/2023]
Abstract
Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.,Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia.,Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Ilknur Tuncer
- Elazig Governorship, Interior Ministry, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia.,School of Women's and Children's Health, University of New South Wales, Randwick 2031, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Aditya P Kamath
- Biomedical Engineering, Brown University, Providence, RI, United States of America
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, United States of America
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, S599489, Singapore.,Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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18
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Khare SK, Bajaj V, Acharya UR. SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiol Meas 2023; 44. [PMID: 36787641 DOI: 10.1088/1361-6579/acbc06] [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: 10/12/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
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Affiliation(s)
- Smith K Khare
- Electrical and Computer Engineering Department, Aarhus University, Denmark
| | - Varun Bajaj
- Discipline of Electronics and Communication Engineering, Indian Institute of Information Technology, Design, and Manufacturing (IIITDM) Jabalpur, India
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.,Department of Biomedical Engineering, School of Science and Technology, University of Social Sciences, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan.,Distinguished Professor, Kumamoto University, Japan.,Adjunct Professor, University of Malaya, Malaysia
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19
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Sharma M, Patel RK, Garg A, SanTan R, Acharya UR. Automated detection of schizophrenia using deep learning: a review for the last decade. Physiol Meas 2023; 44. [PMID: 36630717 DOI: 10.1088/1361-6579/acb24d] [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: 07/12/2022] [Accepted: 01/11/2023] [Indexed: 01/12/2023]
Abstract
Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ruchit Kumar Patel
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Akshat Garg
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ru SanTan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
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20
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Wang Y, Wang J, Su W, Hu H, Xia M, Zhang T, Xu L, Zhang X, Taylor H, Osipowicz K, Young IM, Lin YH, Nicholas P, Tanglay O, Sughrue ME, Tang Y, Doyen S. Symptom-circuit mappings of the schizophrenia connectome. Psychiatry Res 2023; 323:115122. [PMID: 36889161 DOI: 10.1016/j.psychres.2023.115122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. METHODS T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia. RESULTS The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology. CONCLUSION We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen 518000, China; International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Yueh-Hsin Lin
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | | | | | - Michael E Sughrue
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China; Omniscient Neurotechnology, Sydney, Australia
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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Sun L, Wu J, Xu Y, Zhang Y. A federated learning and blockchain framework for physiological signal classification based on continual learning. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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23
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Ruiz de Miras J, Ibáñez-Molina A, Soriano M, Iglesias-Parro S. Schizophrenia classification using machine learning on resting state EEG signal. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104233] [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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Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:1-39. [PMID: 36588765 PMCID: PMC9788870 DOI: 10.1007/s11042-022-14305-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/01/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014-2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.
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Affiliation(s)
- Meghavi Rana
- School of Computing, DIT University, Dehradun, India
| | - Megha Bhushan
- School of Computing, DIT University, Dehradun, India
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Khayretdinova M, Shovkun A, Degtyarev V, Kiryasov A, Pshonkovskaya P, Zakharov I. Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset. Front Aging Neurosci 2022; 14:1019869. [PMID: 36561135 PMCID: PMC9764861 DOI: 10.3389/fnagi.2022.1019869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/02/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Brain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear. Methods The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model. Results The model's training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction. Discussion The architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.
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XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07809-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
AbstractIn clinical practice, algorithmic predictions may seriously jeopardise patients’ health and thus are required to be validated by medical experts before a final clinical decision is met. Towards that aim, there is need to incorporate explainable artificial intelligence techniques into medical research. In the specific field of epileptic seizure detection there are several machine learning algorithms but less methods on explaining them in an interpretable way. Therefore, we introduce XAI4EEG: an application-aware approach for an explainable and hybrid deep learning-based detection of seizures in multivariate EEG time series. In XAI4EEG, we combine deep learning models and domain knowledge on seizure detection, namely (a) frequency bands, (b) location of EEG leads and (c) temporal characteristics. XAI4EEG encompasses EEG data preparation, two deep learning models and our proposed explanation module visualizing feature contributions that are obtained by two SHAP explainers, each explaining the predictions of one of the two models. The resulting visual explanations provide an intuitive identification of decision-relevant regions in the spectral, spatial and temporal EEG dimensions. To evaluate XAI4EEG, we conducted a user study, where users were asked to assess the outputs of XAI4EEG, while working under time constraints, in order to emulate the fact that clinical diagnosis is done - more often than not - under time pressure. We found that the visualizations of our explanation module (1) lead to a substantially lower time for validating the predictions and (2) leverage an increase in interpretability, trust and confidence compared to selected SHAP feature contribution plots.
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27
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Cotes RO, Boazak M, Griner E, Jiang Z, Kim B, Bremer W, Seyedi S, Bahrami Rad A, Clifford GD. Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study. JMIR Res Protoc 2022; 11:e36417. [PMID: 35830230 PMCID: PMC9330209 DOI: 10.2196/36417] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 11/20/2022] Open
Abstract
Background Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient’s outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician’s interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. Objective We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. Methods We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system’s capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system’s sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. Results Data collection began in July 2020 and is expected to continue through December 2022. Conclusions If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. International Registered Report Identifier (IRRID) DERR1-10.2196/36417
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Affiliation(s)
- Robert O Cotes
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Mina Boazak
- Animo Sano Psychiatry, Durham, NC, United States
| | - Emily Griner
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Zifan Jiang
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States.,Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Bona Kim
- Visual Medical Education, Emory School of Medicine, Atlanta, GA, United States
| | - Whitney Bremer
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States
| | - Salman Seyedi
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States.,Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Prabhakar SK, Rajaguru H, Kim C, Won DO. A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification. Front Hum Neurosci 2022; 16:895761. [PMID: 35721347 PMCID: PMC9203681 DOI: 10.3389/fnhum.2022.895761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/02/2022] [Indexed: 12/02/2022] Open
Abstract
The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.
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Affiliation(s)
- Sunil Kumar Prabhakar
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - Chulho Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Chuncheon, South Korea
| | - Dong-Ok Won
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
- *Correspondence: Dong-Ok Won,
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Korda A, Ventouras E, Asvestas P, Toumaian M, Matsopoulos G, Smyrnis N. Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia. Clin Neurophysiol 2022; 139:90-105. [DOI: 10.1016/j.clinph.2022.04.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/11/2022] [Accepted: 04/01/2022] [Indexed: 11/28/2022]
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30
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Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03252-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Al-Ezzi A, Al-Shargabi AA, Al-Shargie F, Zahary AT. Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques. IEEE ACCESS 2022; 10:39926-39938. [DOI: 10.1109/access.2022.3165199] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Affiliation(s)
- Abdulhakim Al-Ezzi
- Electrical and Electronic Engineering Department, Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Bandar, Seri Iskandar, Perak, Malaysia
| | - Amal A. Al-Shargabi
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
| | - Ammar T. Zahary
- Department of Computer Science, Faculty of Computing and IT, University of Science and Technology, Sana’a, Yemen
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A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia. ELECTRONICS 2021. [DOI: 10.3390/electronics10233037] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.
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Shoeibi A, Sadeghi D, Moridian P, Ghassemi N, Heras J, Alizadehsani R, Khadem A, Kong Y, Nahavandi S, Zhang YD, Gorriz JM. Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Front Neuroinform 2021; 15:777977. [PMID: 34899226 PMCID: PMC8657145 DOI: 10.3389/fninf.2021.777977] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.
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Affiliation(s)
- Afshin Shoeibi
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Delaram Sadeghi
- Department of Medical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran
| | - Parisa Moridian
- Faculty of Engineering, Islamic Azad University of Science and Research, Tehran, Iran
| | - Navid Ghassemi
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Logroño, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Ali Khadem
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yinan Kong
- School of Engineering, Macquarie University, Sydney, NSW, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Yu-Dong Zhang
- Department of Informatics, University of Leicester, Leicester, United Kingdom
| | - Juan Manuel Gorriz
- Department of Signal Theory, Telematics and Communications, ETS of Computer and Telecommunications Engineering, University of Granada, Granada, Spain
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Khare SK, Bajaj V. A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Comput Biol Med 2021; 141:105028. [PMID: 34836626 DOI: 10.1016/j.compbiomed.2021.105028] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND Schizophrenia (SCZ) is a serious neurological condition in which people suffer with distorted perception of reality. SCZ may result in a combination of delusions, hallucinations, disordered thinking, and behavior. This causes permanent disability and hampers routine functioning. Trained neurologists use interviewing and visual inspection techniques for the detection and diagnosis of SCZ. These techniques are manual, time-consuming, subjective, and error-prone. Therefore, there is a need to develop an automatic model for SCZ classification. The aim of this study is to develop an automated SCZ classification model using electroencephalogram (EEG) signals. The EEG signals can capture the changes in neural dynamics of human cognition during SCZ. METHOD Based on the nature of the SCZ condition, the EEG signals must be examined. For accurate interpretation of EEG signals during SCZ, an automated model integrating a robust variational mode decomposition (RVMD) and an optimized extreme learning machine (OELM) classifier is developed. Traditional VMD suffers from noisy mode generation, mode duplication, under segmentation, and mode discarding. These problems are suppressed in RVMD by automating the selection of quadratic penalty factor (α) and a number of modes (L). The hyperparameters (HPM) of the OELM classifier are automatically selected to ensure maximum accuracy for each mode without overfitting or underfitting. For the selection of α and L in RVMD and HPM in the OELM classifier, a whale optimization algorithm is used. The root mean square error is minimized for RVMD and classification accuracy of each mode is maximized for the OELM classifier. The EEG signals of three conditions performing basic sensory tasks have been analyzed to detect SCZ. RESULTS The Kruskal Wallis test is used to select different features extracted from the modes produced by RVMD. An OELM classifier is tested using a ten-fold cross-validation technique. An accuracy, precision, specificity, F-1 measure, sensitivity, and Cohen's Kappa of 92.93%, 93.94%, 91.06% 94.07%, 97.15%, and 85.32% are obtained. CONCLUSION The third mode's chaotic features helped to capture the significant changes that occurred during the SCZ state. The findings of the RVMD-OELM-based hybrid decision support system can help neuro-experts for the accurate identification of SCZ in real-time scenarios.
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Affiliation(s)
- Smith K Khare
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India
| | - Varun Bajaj
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India.
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Macpherson T, Churchland A, Sejnowski T, DiCarlo J, Kamitani Y, Takahashi H, Hikida T. Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Netw 2021; 144:603-613. [PMID: 34649035 DOI: 10.1016/j.neunet.2021.09.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain's cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry.
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Affiliation(s)
- Tom Macpherson
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Anne Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - Terry Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, CA, USA; Division of Biological Sciences, University of California San Diego, CA, USA
| | - James DiCarlo
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan.
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Mukhtar H, Qaisar SM, Zaguia A. Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:5456. [PMID: 34450899 PMCID: PMC8402228 DOI: 10.3390/s21165456] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 12/31/2022]
Abstract
Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.
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Affiliation(s)
- Hamid Mukhtar
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Saeed Mian Qaisar
- Electrical and Computer Engineering Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia;
- Communication and Signal Processing Lab, Energy and Technology Research Centre, Effat University, Jeddah 22332, Saudi Arabia
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
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