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Lemoine É, Neves Briard J, Rioux B, Gharbi O, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review. Comput Struct Biotechnol J 2024; 24:66-86. [PMID: 38204455 PMCID: PMC10776381 DOI: 10.1016/j.csbj.2023.12.006] [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: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Background Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Canada
- Institute of biomedical engineering, Polytechnique Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Bastien Rioux
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Oumayma Gharbi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | | | - Bénédicte Nauche
- University of Montreal Hospital Center’s Research Center, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Canada
- School of Public Health, University of Montreal, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frédéric Lesage
- Institute of biomedical engineering, Polytechnique Montreal, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
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Sheng M, Wang S, Zhang Y, Hao R, Liang Y, Luo Y, Yang W, Wang J, Li Y, Zheng W, Li W. A multi-source heterogeneous medical data enhancement framework based on lakehouse. Health Inf Sci Syst 2024; 12:37. [PMID: 38974364 PMCID: PMC11226589 DOI: 10.1007/s13755-024-00295-6] [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: 08/31/2023] [Accepted: 06/17/2024] [Indexed: 07/09/2024] Open
Abstract
Obtaining high-quality data sets from raw data is a key step before data exploration and analysis. Nowadays, in the medical domain, a large amount of data is in need of quality improvement before being used to analyze the health condition of patients. There have been many researches in data extraction, data cleaning and data imputation, respectively. However, there are seldom frameworks integrating with these three techniques, making the dataset suffer in accuracy, consistency and integrity. In this paper, a multi-source heterogeneous data enhancement framework based on a lakehouse MHDP is proposed, which includes three steps of data extraction, data cleaning and data imputation. In the data extraction step, a data fusion technique is offered to handle multi-modal and multi-source heterogeneous data. In the data cleaning step, we propose HoloCleanX, which provides a convenient interactive procedure. In the data imputation step, multiple imputation (MI) and the SOTA algorithm SAITS, are applied for different situations. We evaluate our framework via three tasks: clustering, classification and strategy prediction. The experimental results prove the effectiveness of our data enhancement framework.
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Affiliation(s)
- Ming Sheng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China
| | - Shuliang Wang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China
| | - Yong Zhang
- BNRist, DCST, RIIT, Tsinghua University, Beijing, 100084 China
| | - Rui Hao
- School of Computer Science, International School, Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876 China
| | - Ye Liang
- School of Information Science and Technology, Beijing Foreign Studies University, Beijing, China
| | - Yi Luo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081 China
| | - Wenhan Yang
- School of Computer Science, International School, Beijing University of Posts and Telecommunications, Haidian District, Beijing, 100876 China
| | - Jincheng Wang
- School of Computer and Information Technology, Beijing Jiaotong University, Haidian District, Beijing, 100044 China
| | - Yinan Li
- Dam Safety Monitoring Center, Yellow River Engineering Consulting Co., Ltd, Zhengzhou, 450000 China
| | - Wenkui Zheng
- School of Software, Henan University, Kaifeng, 475004 Henan China
| | - Wenyao Li
- School of Software, Henan University, Kaifeng, 475004 Henan China
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Huang L, Zhou K, Chen S, Chen Y, Zhang J. Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer. Biomed Eng Online 2024; 23:50. [PMID: 38824547 PMCID: PMC11143608 DOI: 10.1186/s12938-024-01244-w] [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: 01/12/2024] [Accepted: 05/08/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios. METHOD To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection. RESULTS The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy. CONCLUSION The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.
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Affiliation(s)
- Leen Huang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Keying Zhou
- Department of Pediatrics, Shenzhen People's Hospital, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Siyang Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yanzhao Chen
- Department of Pediatrics, Shenzhen People's Hospital, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, Second Clinical Medical College of Jinan University, Shenzhen, 518020, Guangdong, China
- Department of Pediatrics, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
| | - Jinxin Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
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Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 DOI: 10.1038/s41598-024-60622-5] [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: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
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Sadiq MT, Siuly S, Almogren A, Li Y, Wen P. Efficient novel network and index for alcoholism detection from EEGs. Health Inf Sci Syst 2023; 11:27. [PMID: 37337563 PMCID: PMC10276798 DOI: 10.1007/s13755-023-00227-w] [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: 12/30/2022] [Accepted: 05/17/2023] [Indexed: 06/21/2023] Open
Abstract
Background Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties. Limitations This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant. Method As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes. Results The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework.
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Affiliation(s)
- Muhammad Tariq Sadiq
- Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK
| | - Siuly Siuly
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 3011 Australia
| | - Ahmad Almogren
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633 Riyadh, Saudi Arabia
| | - Yan Li
- School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia
| | - Paul Wen
- School of Engineering, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia
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6
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Peng Y, Xu Y, Wang M, Zhang H, Xie J. The nnU-Net based method for automatic segmenting fetal brain tissues. Health Inf Sci Syst 2023; 11:17. [PMID: 36998806 PMCID: PMC10043149 DOI: 10.1007/s13755-023-00220-3] [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: 12/24/2022] [Accepted: 03/09/2023] [Indexed: 04/01/2023] Open
Abstract
The magnetic resonance (MR) images of fetuses make it possible for doctors to detect out pathological fetal brains in early stages. Brain tissue segmentation is prerequisite for making brain morphology and volume analyses. nnU-Net is an automatic segmentation method based on deep learning. It can adaptively configure itself, so as to adapt to a specific task via preprocessing, network architecture, training, and post-processing. Therefore, we adapt nnU-Net to segment seven types of fetal brain tissues, including external cerebrospinal fluid, gray matter, white matter, ventricle, cerebellum, deep gray matter, and brainstem. With regard to the characteristics of the FeTA 2021 data, some adjustments are made to the original nnU-Net, so that it can segment seven types of fetal brain tissues precisely as far as possible. The average segmentation results on FeTA 2021 training data demonstrate that our advanced nnU-Net is superior to the peers including SegNet, CoTr, AC U-Net and ResUnet. Its average segmentation results are 0.842, 11.759 and 0.957 in terms of Dice, HD95 and VS criteria. Moreover, the experimental results on FeTA 2021 test data further demonstrate that our advanced nnU-Net has obtained good segmentation performance of 0.774, 14.699 and 0.875 in terms of Dice, HD95 and VS, ranked the third in FeTA 2021 challenge. Our advanced nnU-Net realized the task for segmenting the fetal brain tissues using MR images of different gestational ages, which can help doctors to make correct and seasonable diagnoses.
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Affiliation(s)
- Ying Peng
- School of Computer Science, Shaanxi Normal University, West Chang’an Avenue, Chang’an District, Xi’an, 710119 Shaanxi People’s Republic of China
| | - Yandi Xu
- School of Medicine, Tulane University, 1430 Tulane Ave, New Orleans, LA 70112 USA
| | - Mingzhao Wang
- School of Computer Science, Shaanxi Normal University, West Chang’an Avenue, Chang’an District, Xi’an, 710119 Shaanxi People’s Republic of China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, West Chang’an Avenue, Chang’an District, Xi’an, 710119 Shaanxi People’s Republic of China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, West Chang’an Avenue, Chang’an District, Xi’an, 710119 Shaanxi People’s Republic of China
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7
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Yang J. Using nutrigenomics to guide personalized nutrition supplementation for bolstering immune system. Health Inf Sci Syst 2023; 11:4. [PMID: 36660409 PMCID: PMC9846659 DOI: 10.1007/s13755-022-00208-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 12/27/2022] [Indexed: 01/19/2023] Open
Abstract
Immunity refers to the ability of the human immune system to resist pathogen infection. Immune system has the basic functions of immune defense, immune self stabilization and immune surveillance. Balanced nutrition is the cornerstone of the immune system to play its immune function, and nutritional intervention is also an important means to maintain and improve immunity. Previous studies have confirmed that T cells have individual differences in recognizing viral antigens of virus infected cells, and the body's response to antigens is controlled by a variety of genetic genes, such as human leukocyte antigen (HLA) genes, immune response (Ir) genes, etc. In this paper, through immunity genetic testing, we screen out genetically susceptible people with low immunity and people with the risk of nutrient metabolism disorders; through using lifestyle questionnaire and physical examination results, we analyze people's physical condition, dietary habits, and exercise habits to evaluate people's nutrient deficiency degree. Then, combining multi-dimensional health data, we evaluate users' immune status and nutritional deficiency risk comprehensively, further, we implemented personalized nutrition intervention on the types and doses of nutritional supplements to improve immunity. We also validated the effectiveness of our personalized nutrition solution through a population-based cohort study.
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Affiliation(s)
- Jitao Yang
- School of Information Science, Beijing Language and Culture University, 15 Xueyuan Road, Beijing, 100083 China
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8
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Yang D, Ghafoor U, Eggebrecht AT, Hong KS. Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement. Health Inf Sci Syst 2023; 11:35. [PMID: 37545487 PMCID: PMC10397167 DOI: 10.1007/s13755-023-00233-y] [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: 07/26/2022] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Transcranial alternating current stimulation (tACS) exhibits the capability to interact with endogenous brain oscillations using an external low-intensity sinusoidal current and influences cerebral function. Despite its potential benefits, the physiological mechanisms and effectiveness of tACS are currently a subject of debate and disagreement. The aims of our study are to (i) evaluate the neurological and behavioral impact of tACS by conducting repetitive sham-controlled experiments and (ii) propose criteria to evaluate effectiveness, which can serve as a benchmark to determine optimal individual-based tACS protocols. In this study, 15 healthy adults participated in the experiment over two visiting: sham and tACS (i.e., 5 Hz, 1 mA). During each visit, we used multimodal recordings of the participants' brain, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), along with a working memory (WM) score to quantify neurological effects and cognitive changes immediately after each repetitive sham/tACS session. Our results indicate increased WM scores, hemodynamic response strength, and EEG power in theta and delta bands both during and after the tACS period. Additionally, the observed effects do not increase with prolonged stimulation time, as the effects plateau towards the end of the experiment. In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Moreover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
| | - Adam Thomas Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130 USA
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Institute for Future, School of Automation, Qingdao University, Qingdao, 266071 Shandong China
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Pang X, Ge YF, Wang K, Traina AJM, Wang H. Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm. Health Inf Sci Syst 2023; 11:30. [PMID: 37397165 PMCID: PMC10307766 DOI: 10.1007/s13755-023-00230-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.
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Affiliation(s)
- Xinyu Pang
- Guangdong Technion Israel Institute of Technology, Shantou, China
| | - Yong-Feng Ge
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | - Kate Wang
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia
| | - Agma J. M. Traina
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Paulo, Brazil
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
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Sun Y, Chen X. Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:8078. [PMID: 37836909 PMCID: PMC10575143 DOI: 10.3390/s23198078] [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: 08/29/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.
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Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng 137099, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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11
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Lu X, Wang T, Ye M, Huang S, Wang M, Zhang J. Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics. Front Neurosci 2023; 17:1117340. [PMID: 37214385 PMCID: PMC10192695 DOI: 10.3389/fnins.2023.1117340] [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: 12/06/2022] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
Lots of studies have been carried out on characteristic of epileptic Electroencephalograph (EEG). However, traditional EEG characteristic research methods lack exploration of spatial information. To study the characteristics of epileptic EEG signals from the perspective of the whole brain,this paper proposed combination methods of multi-channel characteristics from time-frequency and spatial domains. This paper was from two aspects: Firstly, signals were converted into 2D Hilbert Spectrum (HS) images which reflected the time-frequency characteristics by Hilbert-Huang Transform (HHT). These images were identified by Convolutional Neural Network (CNN) model whose sensitivity was 99.8%, accuracy was 98.7%, specificity was 97.4%, F1-score was 98.7%, and AUC-ROC was 99.9%. Secondly, the multi-channel signals were converted into brain networks which reflected the spatial characteristics by Symbolic Transfer Entropy (STE) among different channels EEG. And the results show that there are different network properties between ictal and interictal phase and the signals during the ictal enter the synchronization state more quickly, which was verified by Kuramoto model. To summarize, our results show that there was different characteristics among channels for the ictal and interictal phase, which can provide effective physical non-invasive indicators for the identification and prediction of epileptic seizures.
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Affiliation(s)
- Xiaojie Lu
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Tingting Wang
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Mingquan Ye
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Shoufang Huang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
| | - Maosheng Wang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
| | - Jiqian Zhang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
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Al-hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-48. [PMID: 37359338 PMCID: PMC10123593 DOI: 10.1007/s11227-023-05299-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Affiliation(s)
| | - Ali Kadhum M. Al-Qurabat
- Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
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13
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Reus EEM, Visser GH, Sommers-Spijkerman MPJ, van Dijk JG, Cox FME. Automated spike and seizure detection: Are we ready for implementation? Seizure 2023; 108:66-71. [PMID: 37088057 DOI: 10.1016/j.seizure.2023.04.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/06/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023] Open
Abstract
OBJECTIVE Automated detection of spikes and seizures has been a subject of research for several decades now. There have been important advances, yet automated detection in EMU (Epilepsy Monitoring Unit) settings has not been accepted as standard practice. We intend to implement this software at our EMU and so carried out a qualitative study to identify factors that hinder ('barriers') and facilitate ('enablers') implementation. METHOD Twenty-two semi-structured interviews were conducted with 14 technicians and neurologists involved in recording and reporting EEGs and eight neurologists who receive EEG reports in the outpatient department. The study was reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ). RESULTS We identified 14 barriers and 14 enablers for future implementation. Most barriers were reported by technicians. The most prominent barrier was lack of trust in the software, especially regarding seizure detection and false positive results. Additionally, technicians feared losing their EEG review skills or their jobs. Most commonly reported enablers included potential efficiency in the EEG workflow, the opportunity for quantification of EEG findings and the willingness to try the software. CONCLUSIONS This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs.
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Affiliation(s)
- E E M Reus
- Stichting Epilepsie Instellingen Nederland (SEIN).
| | - G H Visser
- Stichting Epilepsie Instellingen Nederland (SEIN)
| | - M P J Sommers-Spijkerman
- Department of Rehabilitation, Physical Therapy Science and Sports, University Medical Center Utrecht, the Netherlands
| | - J G van Dijk
- Department of Neurology, Leiden University Medical Centre, Leiden, the Netherlands
| | - F M E Cox
- Stichting Epilepsie Instellingen Nederland (SEIN)
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14
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Xiao P, Ma K, Gu L, Huang Y, Zhang J, Duan Z, Wang G, Luo Z, Gan X, Yuan J. Inter-subject prediction of pediatric emergence delirium using feature selection and classification from spontaneous EEG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Lemoine É, Neves Briard J, Rioux B, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review. BMJ Open 2023; 13:e066932. [PMID: 36693684 PMCID: PMC9884857 DOI: 10.1136/bmjopen-2022-066932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. METHODS AND ANALYSIS The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies. ETHICS AND DISSEMINATION Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field. PROSPERO REGISTRATION NUMBER CRD42022292261.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bastien Rioux
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Renata Podbielski
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Bénédicte Nauche
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Frédéric Lesage
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, Québec, Canada
| | - Dang K Nguyen
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Montreal, Québec, Canada
- University of Montreal Hospital Centre Research Centre, Montreal, Québec, Canada
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16
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Wang J, Mo Z. Matrix Completion via Successive Low-rank Matrix Approximation. ICST TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023. [DOI: 10.4108/eetsis.v10i3.2878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In this paper, a successive low-rank matrix approximation algorithm is presented for the matrix completion (MC) based on hard thresholding method, which approximate the optimal low-rank matrix from rank-one matrix step by step. The algorithm enables the distance between the matrix with the observed elements and the projection on low-rank manifold to be minimum. The optimal low-rank matrix with observed elements is obtained when the distance is zero. In theory, convergence and convergent error of the new algorithm are analyzed in detail. Furthermore, some numerical experiments show that the algorithm is more effective in CPU time and precision than the orthogonal rank-one matrix pursuit(OR1MP) algorithm and the augmented Lagrange multiplier (ALM) method when the sampling rate is low.
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17
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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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18
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Ramasubramanian B, Reddy VS, Chellappan V, Ramakrishna S. Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases. BIOSENSORS 2022; 12:1176. [PMID: 36551143 PMCID: PMC9775999 DOI: 10.3390/bios12121176] [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: 11/09/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.
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Affiliation(s)
- Brindha Ramasubramanian
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| | - Vijila Chellappan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
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19
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Rahman A, Hossain Z, Kabir E, Rois R. An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh. Health Inf Sci Syst 2022; 10:12. [PMID: 35747767 PMCID: PMC9209612 DOI: 10.1007/s13755-022-00180-0] [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: 01/21/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
We aimed to assess different machine learning techniques for predicting infant mortality (<1 year) in Bangladesh. The decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) approaches were evaluated through accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristics curve and k-fold cross-validation via simulations. The Boruta algorithm and chi-square ( χ 2 ) test were used for features selection of infant mortality. Overall, the RF technique (Boruta: accuracy = 0.8890, sensitivity = 0.0480, specificity = 0.9789, precision = 0.1960, F1-score = 0.0771, AUC = 0.6590; χ 2 : accuracy = 0.8856, sensitivity = 0.0536, specificity = 0.9745, precision = 0.1837, F1-score = 0.0828, AUC = 0.6480) showed higher predictive performance for infant mortality compared to other approaches. Age at first marriage and birth, body mass index (BMI), birth interval, place of residence, religion, administrative division, parents education, occupation of mother, media-exposure, wealth index, gender of child, birth order, children ever born, toilet facility and cooking fuel were potential determinants of infant mortality in Bangladesh. Study findings may help women, stakeholders and policy-makers to take necessary steps for reducing infant mortality by creating awareness, expanding educational programs at community levels and public health interventions.
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Affiliation(s)
- Atikur Rahman
- Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh
| | - Zakir Hossain
- Department of Statistics, University of Dhaka, Dhaka, Bangladesh
| | - Enamul Kabir
- School of Sciences, University of Southern Queensland, Toowoomba, Australia
| | - Rumana Rois
- Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh
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20
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Textural feature based intelligent approach for neurological abnormality detection from brain signal data. PLoS One 2022; 17:e0277555. [DOI: 10.1371/journal.pone.0277555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022] Open
Abstract
The diagnosis of neurological diseases is one of the biggest challenges in modern medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings is usually used to identify various neurological diseases. EEG produces a large volume of multi-channel time-series data that neurologists visually analyze to identify and understand abnormalities within the brain and how they propagate. This is a time-consuming, error-prone, subjective, and exhausting process. Moreover, recent advances in EEG classification have mostly focused on classifying patients of a specific disease from healthy subjects using EEG data, which is not cost effective as it requires multiple systems for checking a subject’s EEG data for different neurological disorders. This forces researchers to advance their work and create a single, unified classification framework for identifying various neurological diseases from EEG signal data. Hence, this study aims to meet this requirement by developing a machine learning (ML) based data mining technique for categorizing multiple abnormalities from EEG data. Textural feature extractors and ML-based classifiers are used on time-frequency spectrogram images to develop the classification system. Initially, noises and artifacts are removed from the signal using filtering techniques and then normalized to reduce computational complexity. Afterwards, normalized signals are segmented into small time segments and spectrogram images are generated from those segments using short-time Fourier transform. Then two histogram based textural feature extractors are used to calculate features separately and principal component analysis is used to select significant features from the extracted features. Finally, four different ML based classifiers are used to categorize those selected features into different disease classes. The developed method is tested on four real-time EEG datasets. The obtained result has shown potential in classifying various abnormality types, indicating that it can be utilized to identify various neurological abnormalities from brain signal data.
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21
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Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
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22
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A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration. Health Inf Sci Syst 2022; 10:22. [PMID: 36039096 PMCID: PMC9417071 DOI: 10.1007/s13755-022-00183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/02/2022] [Indexed: 12/02/2022] Open
Abstract
Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.
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23
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Alvi AM, Siuly S, Wang H, Wang K, Whittaker F. A deep learning based framework for diagnosis of mild cognitive impairment. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved DCE-MRIs. Health Inf Sci Syst 2022; 10:9. [PMID: 35607433 PMCID: PMC9123154 DOI: 10.1007/s13755-022-00176-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022] Open
Abstract
We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.
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25
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Sun Y, Chen X. Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:1322826. [PMID: 35602093 PMCID: PMC9117030 DOI: 10.1155/2022/1322826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/04/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.
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Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130000, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng, Jilin 137000, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jilin 130000, China
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26
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Gao W, Li L, Tao X, Zhou J, Tao J. Identifying informative tweets during a pandemic via a topic-aware neural language model. WORLD WIDE WEB 2022; 26:55-70. [PMID: 35308294 PMCID: PMC8924578 DOI: 10.1007/s11280-022-01034-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/20/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.
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Affiliation(s)
- Wang Gao
- School of Artificial Intelligence, Jianghan University, 430056 Wuhan, China
| | - Lin Li
- School of Computer Science and Artificial Intelligence, Wuhan University of Technology, 430070 Wuhan, China
| | - Xiaohui Tao
- School of Sciences, University of Southern Queensland, 4072 Queensland Toowoomba, Australia
| | - Jing Zhou
- School of Artificial Intelligence, Jianghan University, 430056 Wuhan, China
| | - Jun Tao
- School of Artificial Intelligence, Jianghan University, 430056 Wuhan, China
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27
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Zhang J, Chatzichristos C, Vandecasteele K, Swinnen L, Broux V, Cleeren E, Van Paesschen W, De Vos M. Automatic annotation correction for wearable EEG based epileptic seizure detection. J Neural Eng 2022; 19. [PMID: 35158349 DOI: 10.1088/1741-2552/ac54c1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/14/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to their advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct the imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms. APPROACH This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with 1. original vEEG seizure annotations, 2. visually corrected seizure annotations, and 3. automatically corrected seizure annotations. RESULTS The automatic seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG. SIGNIFICANCE The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.
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Affiliation(s)
- Jingwei Zhang
- Department of Electrical Engineering, STADIUS, KU Leuven, Kasteelpark Arenberg 10, Leuven, Flanders, 3000, BELGIUM
| | - Christos Chatzichristos
- Department of Electrical Engineering, STADIUS, KU Leuven, Kasteelpark Arenberg 10 - box 2446, Leuven, Flanders, 3000, BELGIUM
| | - Kaat Vandecasteele
- Department of Electrical Engineering, STADIUS, KU Leuven, Kasteelpark Arenberg 10, Leuven, Flanders, 3000, BELGIUM
| | - Lauren Swinnen
- KU Leuven, ON V Herestraat 49 - box 1022, Leuven, Flanders, 3000, BELGIUM
| | - Victoria Broux
- Katholieke Universiteit Leuven UZ Leuven, UZ Herestraat 49, Leuven, Flanders, 3000, BELGIUM
| | - Evy Cleeren
- Katholieke Universiteit Leuven UZ Leuven, ON II Herestraat 49 - box 1021, Leuven, Flanders, 3000, BELGIUM
| | - Wim Van Paesschen
- Katholieke Universiteit Leuven UZ Leuven, UZ Herestraat 49 - box 7003, Leuven, Flanders, 3000, BELGIUM
| | - Maarten De Vos
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10 - box 2440, Leuven, Flanders, 3000, BELGIUM
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28
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Sarki R, Ahmed K, Wang H, Zhang Y, Wang K. Automated detection of COVID-19 through convolutional neural network using chest x-ray images. PLoS One 2022; 17:e0262052. [PMID: 35061767 PMCID: PMC8782355 DOI: 10.1371/journal.pone.0262052] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/15/2021] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.
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Affiliation(s)
- Rubina Sarki
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
- * E-mail:
| | - Khandakar Ahmed
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Kate Wang
- RMIT, Melbourne, Victoria, Australia
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29
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ElSayed NE, Tolba AS, Rashad MZ, Belal T, Sarhan S. Multimodal analysis of electroencephalographic and electrooculographic signals. Comput Biol Med 2021; 137:104809. [PMID: 34517160 DOI: 10.1016/j.compbiomed.2021.104809] [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: 06/20/2021] [Revised: 08/24/2021] [Accepted: 08/24/2021] [Indexed: 11/18/2022]
Abstract
Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R2 = 1.00 outperforming the other two approaches with R2 = 0.80 for DT and R2 = 0.76 for SVM.
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Affiliation(s)
- Nesma E ElSayed
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt; Delta Higher Institute for Computers, 35511, Egypt
| | - A S Tolba
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt
| | - M Z Rashad
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt
| | - Tamer Belal
- Department of Neurology, Faculty of Medicine, Mansoura University, 35511, Egypt
| | - Shahenda Sarhan
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt.
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30
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Sarki R, Ahmed K, Wang H, Zhang Y, Ma J, Wang K. Image Preprocessing in Classification and Identification of Diabetic Eye Diseases. DATA SCIENCE AND ENGINEERING 2021; 6:455-471. [PMID: 34423109 PMCID: PMC8370665 DOI: 10.1007/s41019-021-00167-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/17/2021] [Accepted: 07/31/2021] [Indexed: 06/02/2023]
Abstract
Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.
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Affiliation(s)
- Rubina Sarki
- Victoria University, Ballarat Road, Melbourne, VIC 3011 Australia
| | - Khandakar Ahmed
- Victoria University, Ballarat Road, Melbourne, VIC 3011 Australia
| | - Hua Wang
- Victoria University, Ballarat Road, Melbourne, VIC 3011 Australia
| | - Yanchun Zhang
- Victoria University, Ballarat Road, Melbourne, VIC 3011 Australia
| | - Jiangang Ma
- Federation University, Mount Helen, Australia
| | - Kate Wang
- RMIT University, Melbourne, Australia
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31
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Tawhid MNA, Siuly S, Wang H, Whittaker F, Wang K, Zhang Y. A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG. PLoS One 2021; 16:e0253094. [PMID: 34170979 PMCID: PMC8232415 DOI: 10.1371/journal.pone.0253094] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/27/2021] [Indexed: 12/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.
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Affiliation(s)
- Md. Nurul Ahad Tawhid
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
- * E-mail:
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
| | | | - Kate Wang
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria, Australia
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Victoria, Australia
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32
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Liu G, Xiao R, Xu L, Cai J. Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals. Front Syst Neurosci 2021; 15:685387. [PMID: 34093143 PMCID: PMC8173051 DOI: 10.3389/fnsys.2021.685387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/20/2021] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.
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Affiliation(s)
- Guangda Liu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Ruolan Xiao
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Lanyu Xu
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
| | - Jing Cai
- College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China
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33
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Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain. Health Inf Sci Syst 2021; 9:9. [PMID: 33604030 DOI: 10.1007/s13755-021-00139-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 01/13/2021] [Indexed: 02/07/2023] Open
Abstract
A widespread brain disorder of present days is depression which influences 264 million of the world's population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.
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