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Yao B, Wu C, Zhang X, Yao J, Xue J, Zhao Y, Li T, Pu J. The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action. SENSORS (BASEL, SWITZERLAND) 2024; 24:2323. [PMID: 38610534 PMCID: PMC11014078 DOI: 10.3390/s24072323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/02/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024]
Abstract
This study explores the important role of assessing force levels in accurately controlling upper limb movements in human-computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human-computer interface technologies.
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Affiliation(s)
- Bo Yao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
| | - Chengzhen Wu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
- School of Life Sciences, Tiangong University, Tianjin 300387, China
| | - Xing Zhang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
| | - Junjie Yao
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
| | - Jianchao Xue
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (J.X.)
| | - Yu Zhao
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; (J.X.)
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
| | - Jiangbo Pu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China; (B.Y.); (C.W.); (X.Z.); (J.Y.)
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2
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Huang Y, Zhao Y, Capstick A, Palermo F, Haddadi H, Barnaghi P. Analyzing entropy features in time-series data for pattern recognition in neurological conditions. Artif Intell Med 2024; 150:102821. [PMID: 38553161 DOI: 10.1016/j.artmed.2024.102821] [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: 06/02/2023] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
In the field of medical diagnosis and patient monitoring, effective pattern recognition in neurological time-series data is essential. Traditional methods predominantly based on statistical or probabilistic learning and inference often struggle with multivariate, multi-source, state-varying, and noisy data while also posing privacy risks due to excessive information collection and modeling. Furthermore, these methods often overlook critical statistical information, such as the distribution of data points and inherent uncertainties. To address these challenges, we introduce an information theory-based pipeline that leverages specialized features to identify patterns in neurological time-series data while minimizing privacy risks. We incorporate various entropy methods based on the characteristics of different scenarios and entropy. For stochastic state transition applications, we incorporate Shannon's entropy, entropy rates, entropy production, and the von Neumann entropy of Markov chains. When state modeling is impractical, we select and employ approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The pipeline's effectiveness and scalability are demonstrated through pattern analysis in a dementia care dataset and also an epileptic and a myocardial infarction dataset. The results indicate that our information theory-based pipeline can achieve average performance improvements across various models on the recall rate, F1 score, and accuracy by up to 13.08 percentage points, while enhancing inference efficiency by reducing the number of model parameters by an average of 3.10 times. Thus, our approach opens a promising avenue for improved, efficient, and critical statistical information-considered pattern recognition in medical time-series data.
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Affiliation(s)
- Yushan Huang
- Dyson School of Design Engineering, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Yuchen Zhao
- Department of Computer Science, University of York, York, UK
| | - Alexander Capstick
- Department of Brain Sciences, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Francesca Palermo
- Department of Brain Sciences, Imperial College London, London, UK; Great Ormond Street Hospital for Children, London, UK
| | - Hamed Haddadi
- Department of Computing, Imperial College London, London, UK
| | - Payam Barnaghi
- Department of Brain Sciences, Imperial College London, London, UK; The Great Ormond Street Institute of Child Health, University College London, London, UK; Great Ormond Street Hospital for Children, London, UK; Care Research and Technology Centre, The UK Dementia Research Institute, London, UK.
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3
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Srinivasan S, Johnson SD. Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory. Cogn Neurodyn 2024; 18:431-446. [PMID: 38699607 PMCID: PMC11061098 DOI: 10.1007/s11571-023-10011-x] [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: 03/15/2023] [Revised: 06/06/2023] [Accepted: 09/16/2023] [Indexed: 05/05/2024] Open
Abstract
Schizophrenia (SZ) is a mental disorder that causes lifelong disorders based on delusions, cognitive deficits, and hallucinations. By visual assessment, SZ diagnosis is time-consuming and complicated, because brain states are more effectively revealed by electroencephalogram (EEG) signals, which are effectively used in SZ diagnosis. The application of existing deep learning methods in SZ detection is effective in the classification of 2-dimensional images, and these methods require more computational resources. Therefore, dimensionality reduction is necessary for SZ diagnosis using EEG signals. To reduce the dimensionality of the data, an improved CAO (ICAO) dimensionality reduction method is proposed, which integrates horizontal and vertical crossover approaches with AOA. The optimal feature subset is achieved by satisfying the ICAO conditions, and a fitness function is evaluated based on rough sets for improved accuracy in feature selection. Therefore a Crossover-boosted Archimedes optimization algorithm (AOA) with rough sets for Schizophrenia detection (CAORS-SD) was proposed using multichannel EEG signals from both SZ and normal patients. The signals are decomposed using multivariate empirical mode decomposition into multivariate intrinsic mode functions (MIMFs). Entropy metrics such as spectral entropy, permutation entropy, approximate entropy, sample entropy, and SVD entropy are evaluated on the MIMF domain to detect SZ. The processing time of the kernel support vector machine classifier is minimized with fewer features, reducing the risk Fof overfitting. Accuracy, sensitivity, specificity, precision, and F1-score of the CAORS-SD model should be conducted to diagnose SZ. Therefore, the proposed CAORS-SD method achieves the higher performance of accuracy, sensitivity, specificity, precision, and F1-score values of 96.34, 98.95, 96.86, 98.52, and 96.74% respectively. Also, the CAORS-SD method minimizes the error rate and significantly reduces the execution time.
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4
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Guo Z, Wang J, Jing T, Fu L. Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108105. [PMID: 38447316 DOI: 10.1016/j.cmpb.2024.108105] [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: 10/25/2023] [Revised: 02/07/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. METHODS This study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data. RESULTS We employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients. CONCLUSIONS This emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.
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Affiliation(s)
- Zhifen Guo
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Jiao Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Tianyu Jing
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Longyue Fu
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
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5
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Lal U, Chikkankod AV, Longo L. A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults. Brain Sci 2024; 14:335. [PMID: 38671987 PMCID: PMC11048688 DOI: 10.3390/brainsci14040335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.
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Affiliation(s)
- Utkarsh Lal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India;
| | - Arjun Vinayak Chikkankod
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
- Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland
| | - Luca Longo
- Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland
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6
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Yin G, Chang Y, Zhao Y, Liu C, Yin M, Fu Y, Shi D, Wang L, Jin L, Huang J, Li D, Niu Y, Wang B, Tan S. Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network. Asian J Psychiatr 2023; 87:103687. [PMID: 37418809 DOI: 10.1016/j.ajp.2023.103687] [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] [Received: 04/11/2023] [Revised: 06/25/2023] [Accepted: 06/30/2023] [Indexed: 07/09/2023]
Abstract
Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.
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Affiliation(s)
- Guimei Yin
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Ying Chang
- Departs of Ultrasonography, Xuan Wu Hospital, Capital Medical University, Beijing 100053, China
| | - Yanli Zhao
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Chenxu Liu
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Mengzhen Yin
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Yongcan Fu
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Dongli Shi
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Lin Wang
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Lizhong Jin
- Taiyuan University of Science and Technology, Taiyuan 030024 Shanxi, China
| | - Jie Huang
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Dandan Li
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China
| | - Yan Niu
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China
| | - Bin Wang
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China.
| | - Shuping Tan
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China.
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7
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Sun Y, Yang C, Xu Z, Lu Y. Recurrence Plot-Assisted Detection of Focal/Non-focal EEG Signals Using Ensemble Deep Features. J Med Biol Eng 2023. [DOI: 10.1007/s40846-023-00785-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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8
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Yang L, Wei X, Liu F, Zhu X, Zhou F. Automatic feature learning model combining functional connectivity network and graph regularization for depression detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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9
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Siuly S, Guo Y, Alcin OF, Li Y, Wen P, Wang H. Exploring deep residual network based features for automatic schizophrenia detection from EEG. Phys Eng Sci Med 2023; 46:561-574. [PMID: 36947384 DOI: 10.1007/s13246-023-01225-8] [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: 11/02/2022] [Accepted: 01/16/2023] [Indexed: 03/23/2023]
Abstract
Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.
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Affiliation(s)
- Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia.
- Centre for Health Research, University of Southern Queensland, Toowoomba, Australia.
| | - Yanhui Guo
- Department of Computer Science, University of Illinois at Springfield, Springfield, IL, 62703, USA
| | - Omer Faruk Alcin
- Department of Electrical-Electronics Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Malatya, Turkey
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Peng Wen
- School of Engineering, University of Southern Queensland, Toowoomba, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
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10
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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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11
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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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12
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Agarwal M, Singhal A. Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals. Med Eng Phys 2023; 112:103949. [PMID: 36842772 DOI: 10.1016/j.medengphy.2023.103949] [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: 06/29/2022] [Revised: 12/01/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023]
Abstract
Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.
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Affiliation(s)
- Megha Agarwal
- Department of Electronics & Communication Engineering, Jaypee Institute of Information Technology, Noida, India.
| | - Amit Singhal
- Department of Electronics & Communication Engineering, Netaji Subhas University of Technology, Delhi, India.
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13
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Zheng J, Li Y, Zhai Y, Zhang N, Yu H, Tang C, Yan Z, Luo E, Xie K. Effects of sampling rate on multiscale entropy of electroencephalogram time series. Biocybern Biomed Eng 2023. [DOI: 10.1016/j.bbe.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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14
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Jiang L, He J, Pan H, Wu D, Jiang T, Liu J. Seizure detection algorithm based on improved functional brain network structure feature extraction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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15
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Manic KS, Rajinikanth V, Al-Bimani AS, Taniar D, Kadry S. Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features. SENSORS (BASEL, SWITZERLAND) 2022; 23:280. [PMID: 36616876 PMCID: PMC9823879 DOI: 10.3390/s23010280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.
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Affiliation(s)
- K. Suresh Manic
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Ali Saud Al-Bimani
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - David Taniar
- Faculty of Information Technology, Monash University, Wellington Rd, Clayton, VIC 3800, Australia
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon
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16
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Parija S, Sahani M, Bisoi R, Dash PK. Autoencoder-based improved deep learning approach for schizophrenic EEG signal classification. Pattern Anal Appl 2022. [DOI: 10.1007/s10044-022-01107-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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17
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Liu J, Tian K, Xiong H, Zheng Y. A fast and efficient algorithm for multi-channel transcranial magnetic stimulation (TMS) signal denoising. Med Biol Eng Comput 2022; 60:2479-2492. [DOI: 10.1007/s11517-022-02616-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 06/15/2022] [Indexed: 10/16/2022]
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18
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Bagherzadeh S, Shahabi MS, Shalbaf A. Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 2022; 146:105570. [DOI: 10.1016/j.compbiomed.2022.105570] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/14/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023]
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19
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Abidi A, Ben Khalifa K, Ben Cheikh R, Valderrama Sakuyama CA, Bedoui MH. Automatic Detection of Drowsiness in EEG Records Based on Machine Learning Approaches. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10858-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Aydemir E, Dogan S, Baygin M, Ooi CP, Barua PD, Tuncer T, Acharya UR. CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals. Healthcare (Basel) 2022; 10:healthcare10040643. [PMID: 35455821 PMCID: PMC9027158 DOI: 10.3390/healthcare10040643] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/18/2022] [Accepted: 03/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.
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Affiliation(s)
- Emrah Aydemir
- Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya 54050, Turkey;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey;
- Correspondence: ; Tel.: +90-424-2370000-7634
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey;
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 413, Taiwan
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21
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ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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22
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Wang X, Liu X, Pang W, Jiang A. Multiscale increment entropy: An approach for quantifying the physiological complexity of biomedical time series. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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23
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Chen J, Li H, Ma L, Soong F. DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals. Front Psychiatry 2022; 13:885120. [PMID: 35573327 PMCID: PMC9091650 DOI: 10.3389/fpsyt.2022.885120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) is one of the most widely-used biosignal capturing technology for investigating brain activities, cognitive diseases, and affective disorders. To understand the underlying principles of brain activities and affective disorders using EEG data, one of the fundamental tasks is to accurately identify emotions from EEG signals, which has attracted huge attention in the field of affective computing. To improve the accuracy and effectiveness of emotion recognition based on EEG data, previous studies have successfully developed numerous feature extraction methods and classifiers. Among them, ensemble empirical mode decomposition (EEMD) is an efficient signal decomposition technique for extracting EEG features. It can alleviate the mode-mixing problem by adding white noise to the source signal. However, there remain some issues when applying this method to recognition tasks. As the added noise cannot be filtered completely, spurious modes are generated due to the residual noise. Therefore, it is crucial to perform intrinsic mode function (IMF) selection to find the most valuable IMF components that represent brain activities. Furthermore, the number of decomposed IMFs is various to different original signals, thus how to unify feature dimensions needs better solutions. To solve these issues, we propose a novel forecasting framework, named DEEMD-SPP, to identify emotions from EEG signals, based on the combination of denoising ensemble empirical mode decomposition (DEEMD) and Spatial Pyramid Pooling Network (SPP-Net). First, DEEMD is proposed to decompose the EEG signals, which effectively eliminates residual noise in the IMFs and selects the most valuable IMFs. Second, time-domain and frequency-domain features are extracted from the selected IMFs. Finally, SPP-net is employed as the classifier to recognize emotions, which can effectively transform various-sized feature maps into fixed-sized feature vectors through the pyramid pooling layer. The experimental results demonstrate that our proposed DEEMD-SPP framework can effectively reduce the effect of spike-in white noise, accurately extract EEG features, and significantly improve the performance of emotion recognition.
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Affiliation(s)
- Jing Chen
- School of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- School of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Lin Ma
- School of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Frank Soong
- School of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology, Harbin, China.,Speech Group, Microsoft Research Asia, Beijing, China
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Prabhakar SK, Lee SW. SASDL and RBATQ: Sparse Autoencoder with Swarm based Deep Learning and Reinforcement based Q-learning for EEG Classification. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:58-68. [PMID: 35770240 PMCID: PMC9135179 DOI: 10.1109/ojemb.2022.3161837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/20/2022] [Accepted: 03/17/2022] [Indexed: 11/23/2022] Open
Abstract
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. Goal: In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. Methods: The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Results and Conclusions: Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.
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Affiliation(s)
| | - Seong-Whan Lee
- Department of Artificial IntelligenceKorea University Seoul 02841 South Korea
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25
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Ilakiyaselvan N, Khan AN, Shahina A. Reconstructed phase space portraits for detecting brain diseases using deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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26
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Gu H, Chou CA. Optimizing non-uniform multivariate embedding for multiscale entropy analysis of complex systems. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103206] [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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27
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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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28
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Orhanbulucu F, Latifoğlu F. Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method. Comput Methods Biomech Biomed Engin 2021; 25:840-851. [PMID: 34602001 DOI: 10.1080/10255842.2021.1983803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naïve Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.
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Affiliation(s)
- Fırat Orhanbulucu
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey.,Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - Fatma Latifoğlu
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
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29
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Baygin M, Yaman O, Tuncer T, Dogan S, Barua PD, Acharya UR. Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102936] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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30
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Chang Q, Li C, Tian Q, Bo Q, Zhang J, Xiong Y, Wang C. Classification of First-Episode Schizophrenia, Chronic Schizophrenia and Healthy Control Based on Brain Network of Mismatch Negativity by Graph Neural Network. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1784-1794. [PMID: 34406943 DOI: 10.1109/tnsre.2021.3105669] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mismatch negativity (MMN) has been consistently found deficit in schizophrenia, which was considered as a promising biomarker for assessing the impairments in pre-attentive auditory processing. However, the functional connectivity between brain regions based on MMN is not clear. This study provides an in-depth investigation in brain functional connectivity during MMN process among patients with first-episode schizophrenia (FESZ), chronic schizophrenia (CSZ) and healthy control (HC). Electroencephalography (EEG) data of 128 channels is recorded during frequency and duration MMN in 40 FESZ, 40 CSZ patients and 40 matched HC subjects. We reconstruct the cortical endogenous electrical activity from EEG recordings using exact low-resolution electromagnetic tomography and build functional brain networks based on source-level EEG data. Then, graph-theoretic features are extracted from the brain networks with the support vector machine (SVM) to classify FESZ, CSZ and HC groups, since the SVM has good generalization ability and robustness as a universally applicable nonlinear classifier. Furthermore, we introduce the graph neural network (GNN) model to directly learn for the network topology of brain network. Compared to HC, the damaged brain areas of CSZ are more extensive than FESZ, and the damaged area involved the auditory cortex. These results demonstrate the heterogeneity of the impacts of schizophrenia for different disease courses and the association between MMN and the auditory cortex. More importantly, the GNN classification results are significantly better than those of SVM, and hence the EEG-based GNN model of brain networks provides an effective method for discriminating among FESZ, CSZ and HC groups.
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31
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Akbari H, Ghofrani S, Zakalvand P, Tariq Sadiq M. Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102917] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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32
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Baygin M. An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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33
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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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34
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Das K, Pachori RB. Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102525] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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35
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Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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36
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37
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Murugappan M, Alshuaib W, Bourisly AK, Khare SK, Sruthi S, Bajaj V. Tunable Q wavelet transform based emotion classification in Parkinson's disease using Electroencephalography. PLoS One 2020; 15:e0242014. [PMID: 33211717 PMCID: PMC7676721 DOI: 10.1371/journal.pone.0242014] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 10/25/2020] [Indexed: 12/20/2022] Open
Abstract
Parkinson's disease (PD) is a severe incurable neurological disorder. It is mostly characterized by non-motor symptoms like fatigue, dementia, anxiety, speech and communication problems, depression, and so on. Electroencephalography (EEG) play a key role in the detection of the true emotional state of a person. Various studies have been proposed for the detection of emotional impairment in PD using filtering, Fourier transforms, wavelet transforms, and non-linear methods. However, these methods require a selection of basis and are confined in terms of accuracy. In this paper, tunable Q wavelet transform (TQWT) is proposed for the classification of emotions in PD and normal controls (NC). EEG signals of six emotional states namely happiness, sadness, fear, anger, surprise, and disgust are studied. Power, entropy, and statistical moments based features are elicited from the highpass and lowpass sub-bands of TQWT. Six features selected by statistical analysis are classified with a k-nearest neighbor, probabilistic neural network, random forest, decision tree, and extreme learning machine. Three performance measures are obtained, maximum mean accuracy, sensitivity, and specificity of 96.16%, 97.59%, and 88.51% for NC and 93.88%, 96.33%, and 81.67% for PD are achieved with a probabilistic neural network. The proposed method proved to be very effective such that it classifies emotions in PD and could be used as a potential tool for diagnosing emotional impairment in hospitals.
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Affiliation(s)
- Murugappan Murugappan
- Intelligent Signal Processing Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (A Private University), Doha, Kuwait
| | - Waleed Alshuaib
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Ali K. Bourisly
- Department of Physiology, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Smith K. Khare
- Department of Electronics and Communication, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - Sai Sruthi
- Intelligent Signal Processing Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (A Private University), Doha, Kuwait
| | - Varun Bajaj
- Department of Electronics and Communication, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
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