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Wang Z, Li S, Luo J, Liu J, Wu D. Channel reflection: Knowledge-driven data augmentation for EEG-based brain-computer interfaces. Neural Netw 2024; 176:106351. [PMID: 38713969 DOI: 10.1016/j.neunet.2024.106351] [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: 12/30/2023] [Revised: 04/04/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.
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Affiliation(s)
- Ziwei Wang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
| | - Siyang Li
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
| | - Jingwei Luo
- China Electronic System Technology Co., Ltd., Beijing 100089, China.
| | - Jiajing Liu
- School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Dongrui Wu
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen 518063, China.
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2
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Lv H, Zhang Y, Xiao T, Wang Z, Wang S, Feng H, Zhao X, Zhao Y. Seizure Detection Based on Lightweight Inverted Residual Attention Network. Int J Neural Syst 2024; 34:2450042. [PMID: 38818805 DOI: 10.1142/s0129065724500424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detection. Additionally, existing methods often neglect the key characteristic channels and spatial regions of electroencephalography (EEG) signals. To solve these issues, we propose a lightweight EEG-based seizure detection model named lightweight inverted residual attention network (LRAN). Specifically, we employ a four-stage inverted residual mobile block (iRMB) to effectively extract the hierarchical features from EEG. The convolutional block attention module (CBAM) is introduced to make the model focus on important feature channels and spatial information, thereby enhancing the discrimination of the learned features. Finally, convolution operations are used to capture local information and spatial relationships between features. We conduct intra-subject and inter-subject experiments on a publicly available dataset. Intra-subject experiments obtain 99.25% accuracy in segment-based detection and 0.36/h false detection rate (FDR) in event-based detection, respectively. Inter-subject experiments obtain 84.32% accuracy. Both sets of experiments maintain high classification accuracy with a low number of parameters, where the multiply accumulate operations (MACs) are 25.86[Formula: see text]M and the number of parameters is 0.57[Formula: see text]M.
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Affiliation(s)
- Hongbin Lv
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yongfeng Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Tiantian Xiao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Ziwei Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shuai Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Hailing Feng
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Xianxun Zhao
- Department of Automotive Engineering, Heze Engineering Technician College, Heze 274000, P. R. China
| | - Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
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3
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Pfeffer MA, Ling SSH, Wong JKW. Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces. Comput Biol Med 2024; 178:108705. [PMID: 38865781 DOI: 10.1016/j.compbiomed.2024.108705] [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: 11/11/2023] [Revised: 05/01/2024] [Accepted: 06/01/2024] [Indexed: 06/14/2024]
Abstract
This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems.
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Affiliation(s)
- Maximilian Achim Pfeffer
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Steve Sai Ho Ling
- Faculty of Engineering and Information Technology, University of Technology Sydney, CB11 81-113, Broadway, Ultimo, 2007, New South Wales, Australia.
| | - Johnny Kwok Wai Wong
- Faculty of Design, Architecture and Building, University of Technology Sydney, 15 Broadway, Ultimo, 2007, New South Wales, Australia.
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4
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Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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5
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Benzaid A, Djemili R, Arbateni K. Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers. Comput Methods Biomech Biomed Engin 2024:1-17. [PMID: 38803055 DOI: 10.1080/10255842.2024.2356634] [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: 01/10/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024]
Abstract
Epilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature.
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Affiliation(s)
- Amel Benzaid
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
| | - Rafik Djemili
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
| | - Khaled Arbateni
- LRES Lab, Universite 20 Aout 1955 Skikda Faculte de Technologie, Skikda, Algeria
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6
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Islam T, Islam R, Basak M, Roy AD, Arman MA, Paul S, Shandra O, Ali SR. Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier. Sci Rep 2024; 14:10792. [PMID: 38734752 PMCID: PMC11088643 DOI: 10.1038/s41598-024-61338-2] [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: 01/17/2024] [Accepted: 05/04/2024] [Indexed: 05/13/2024] Open
Abstract
Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.
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Affiliation(s)
- Torikul Islam
- Department of Biomedical Engineering (BME), New Jersey Institute of Technology, Newark, NJ, USA.
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology, Khulna, 9230, Bangladesh.
| | - Redwanul Islam
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology, Khulna, 9230, Bangladesh
| | - Monisha Basak
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology, Khulna, 9230, Bangladesh
| | - Amit Dutta Roy
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology, Khulna, 9230, Bangladesh
| | - Md Adil Arman
- Department of Biomedical Engineering (BME), Florida International University, Miami, FL, USA
| | - Samanta Paul
- Department of Biomedical Engineering (BME), University of Cincinnati, Cincinnati, OH, USA
| | - Oleksii Shandra
- Department of Biomedical Engineering (BME), Florida International University, Miami, FL, USA
| | - Sk Rahat Ali
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology, Khulna, 9230, Bangladesh
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7
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Marathe M. Therapeutic value in the time of digital brainwaves. SOCIAL STUDIES OF SCIENCE 2024:3063127241241032. [PMID: 38584390 DOI: 10.1177/03063127241241032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
This article examines the value of medical technology through the case of electroencephalograms (EEGs), devices used to visualize brain activity and diagnose seizures. Drawing on ethnographic fieldwork, the article shows that EEGs are valued differently by patients and medical practitioners. While practitioners value EEGs for their clinical utility, i.e., ability to inform clinical decisions, patients value EEGs even in the absence of clinical utility. Indeed, patients derive long-lasting therapeutic effects from this diagnostic technology. These findings intervene in the utilitarian calculus of therapeutic value-a mode of reasoning that equates value with clinical utility-commonly deployed in biomedicine and engineering and call for a recognition of alternative notions such as the therapeutic value of being witnessed and cared for by medical experts via EEGs and other technologies that require time to work. Expansive notions of therapeutic value are imperative for including marginalized patients-especially low-income, disabled, and women patients-in debates on automation and the future of healthcare. Studying how multiple stakeholders value a medical technology provides insight into valuation, objectification, expertise, and other concerns central to science and technology studies.
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Affiliation(s)
- Megh Marathe
- Michigan State University, East Lansing, MI, USA
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8
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Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [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: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
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Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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9
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Karimi-Rouzbahani H, McGonigal A. Generalisability of epileptiform patterns across time and patients. Sci Rep 2024; 14:6293. [PMID: 38491096 PMCID: PMC10942983 DOI: 10.1038/s41598-024-56990-7] [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: 01/20/2024] [Accepted: 03/13/2024] [Indexed: 03/18/2024] Open
Abstract
The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia.
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia.
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia
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10
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [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: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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11
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Pancholi S, Everett TH, Duerstock BS. Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI. Sci Rep 2024; 14:3439. [PMID: 38341453 PMCID: PMC10858945 DOI: 10.1038/s41598-024-53718-5] [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: 07/06/2023] [Accepted: 02/04/2024] [Indexed: 02/12/2024] Open
Abstract
This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring.
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Affiliation(s)
- Sidharth Pancholi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Thomas H Everett
- Krannert Cardiovascular Research Center, Division of Cardiovascular Medicine, IU School of Medicine, Indianapolis, USA
| | - Bradley S Duerstock
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
- School of Industrial Engineering, Purdue University, West Lafayette, USA.
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12
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Rakhmatulin I, Dao MS, Nassibi A, Mandic D. Exploring Convolutional Neural Network Architectures for EEG Feature Extraction. SENSORS (BASEL, SWITZERLAND) 2024; 24:877. [PMID: 38339594 PMCID: PMC10856895 DOI: 10.3390/s24030877] [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: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/12/2024]
Abstract
The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.
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Affiliation(s)
- Ildar Rakhmatulin
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Minh-Son Dao
- National Institute of Information and Communications Technology (NICT), Tokyo 184-0015, Japan
| | - Amir Nassibi
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
| | - Danilo Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.)
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13
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Lee D, Kim B, Kim T, Joe I, Chong J, Min K, Jung K. A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning. Sci Rep 2024; 14:1319. [PMID: 38225340 PMCID: PMC10789752 DOI: 10.1038/s41598-023-43328-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 09/22/2023] [Indexed: 01/17/2024] Open
Abstract
In this paper, we propose a method for predicting epileptic seizures using a pre-trained model utilizing supervised contrastive learning and a hybrid model combining residual networks (ResNet) and long short-term memory (LSTM). The proposed training approach encompasses three key phases: pre-processing, pre-training as a pretext task, and training as a downstream task. In the pre-processing phase, the data is transformed into a spectrogram image using short time Fourier transform (STFT), which extracts both time and frequency information. This step compensates for the inherent complexity and irregularity of electroencephalography (EEG) data, which often hampers effective data analysis. During the pre-training phase, augmented data is generated from the original dataset using techniques such as band-stop filtering and temporal cutout. Subsequently, a ResNet model is pre-trained alongside a supervised contrastive loss model, learning the representation of the spectrogram image. In the training phase, a hybrid model is constructed by combining ResNet, initialized with weight values from the pre-trained model, and LSTM. This hybrid model extracts image features and time information to enhance prediction accuracy. The proposed method's effectiveness is validated using datasets from CHB-MIT and Seoul National University Hospital (SNUH). The method's generalization ability is confirmed through Leave-one-out cross-validation. From the experimental results measuring accuracy, sensitivity, and false positive rate (FPR), CHB-MIT was 91.90%, 89.64%, 0.058 and SNUH was 83.37%, 79.89%, and 0.131. The experimental results demonstrate that the proposed method outperforms the conventional methods.
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Affiliation(s)
- Dohyun Lee
- Department of Computer Science, Hanyang University, Seoul, 04763, South Korea
| | - Byunghyun Kim
- Department of Computer Science, Hanyang University, Seoul, 04763, South Korea
| | - Taejoon Kim
- Department of Neurology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Inwhee Joe
- Department of Computer Science, Hanyang University, Seoul, 04763, South Korea
| | - Jongwha Chong
- Department of Computer Science, State University of New York Korea, Incheon, 21985, South Korea
| | - Kyeongyuk Min
- Department of Electronics Engineering, Hanyang University, Seoul, 04763, South Korea.
| | - Kiyoung Jung
- Department of Neurology, Seoul National University College of Medicine, Seoul, 03080, South Korea.
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14
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Jibon FA, Jamil Chowdhury AR, Miraz MH, Jin HH, Khandaker MU, Sultana S, Nur S, Siddiqui FH, Kamal AHM, Salman M, Youssef AAF. Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal. Digit Health 2024; 10:20552076241249874. [PMID: 38726217 PMCID: PMC11080778 DOI: 10.1177/20552076241249874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 05/12/2024] Open
Abstract
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.
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Affiliation(s)
- Ferdaus Anam Jibon
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - A. R. Jamil Chowdhury
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Mahadi Hasan Miraz
- Department of Management, Marketing and Digital Business, Faculty of Business, Curtin University Malaysia, Miri, Malaysia
| | - Hwang Ha Jin
- Department of Business Analytics, Sunway University, Bandar Sunway, Selangor, Malaysia
| | - Mayeen Uddin Khandaker
- Applied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor, Malaysia
- Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka, Bangladesh
| | - Sajia Sultana
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Sifat Nur
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Fazlul Hasan Siddiqui
- Department of Computer Science & Engineering, Dhaka
University of Engineering & Technology (DUET), Gazipur, Dhaka, Bangladesh
| | - AHM Kamal
- Department of Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University (JKKNIU), Trishal, Mymensingh, Bangladesh
| | - Mohammad Salman
- College of Engineering and Technology, American University of the Middle East, Kuwait
| | - Ahmed A. F. Youssef
- College of Engineering and Technology, American University of the Middle East, Kuwait
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15
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Vieira JC, Guedes LA, Santos MR, Sanchez-Gendriz I. Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:9871. [PMID: 38139715 PMCID: PMC10747117 DOI: 10.3390/s23249871] [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: 09/29/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/24/2023]
Abstract
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.
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Affiliation(s)
- Jusciaane Chacon Vieira
- Department of Computer Engineering and Automation—DCA, Federal University of Rio Grande do Norte—UFRN, Natal 59078-900, RN, Brazil; (L.A.G.); (M.R.S.); (I.S.-G.)
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16
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Golla SK, Maloji S. A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3769. [PMID: 37740655 DOI: 10.1002/cnm.3769] [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: 04/05/2023] [Revised: 08/04/2023] [Accepted: 08/18/2023] [Indexed: 09/24/2023]
Abstract
Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time-consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non-linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time-frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE-GTRN-LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE-based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure-affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE-GTRN-LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state-of-the-art techniques.
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Affiliation(s)
- Sandhya Kumari Golla
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India
| | - Suman Maloji
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India
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17
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Wang Z, Zhang W, Li S, Chen X, Wu D. Unsupervised domain adaptation for cross-patient seizure classification. J Neural Eng 2023; 20:066002. [PMID: 37906968 DOI: 10.1088/1741-2552/ad0859] [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: 06/09/2023] [Accepted: 10/31/2023] [Indexed: 11/02/2023]
Abstract
Objective. Epileptic seizure is a chronic neurological disease affecting millions of patients. Electroencephalogram (EEG) is the gold standard in epileptic seizure classification. However, its low signal-to-noise ratio, strong non-stationarity, and large individual difference nature make it difficult to directly extend the seizure classification model from one patient to another. This paper considers multi-source unsupervised domain adaptation for cross-patient EEG-based seizure classification, i.e. there are multiple source patients with labeled EEG data, which are used to label the EEG trials of a new patient.Approach. We propose an source domain selection (SDS)-global domain adaptation (GDA)-target agent subdomain adaptation (TASA) approach, which includes SDS to filter out dissimilar source domains, GDA to align the overall distributions of the selected source domains and the target domain, and TASA to identify the most similar source domain to the target domain so that its labels can be utilized.Main results. Experiments on two public seizure datasets demonstrated that SDS-GDA-TASA outperformed 13 existing approaches in unsupervised cross-patient seizure classification.Significance. Our approach could save clinicians plenty of time in labeling EEG data for epilepsy patients, greatly increasing the efficiency of seizure diagnostics.
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Affiliation(s)
- Ziwei Wang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Wen Zhang
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Siyang Li
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Xinru Chen
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
| | - Dongrui Wu
- Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
- Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
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18
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Djemili R, Djemili I. Nonlinear and chaos features over EMD/VMD decomposition methods for ictal EEG signals detection. Comput Methods Biomech Biomed Engin 2023:1-20. [PMID: 37861376 DOI: 10.1080/10255842.2023.2271603] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/08/2023] [Indexed: 10/21/2023]
Abstract
The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.
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Affiliation(s)
| | - Ilyes Djemili
- Lab. Electrotech, Université 20 Août, Skikda, Algeria
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19
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Belyaev M, Murugappan M, Velichko A, Korzun D. Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:8609. [PMID: 37896703 PMCID: PMC10610702 DOI: 10.3390/s23208609] [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: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0-4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD.
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Affiliation(s)
- Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Kuwait City 13133, Kuwait;
- Department of Electronics and Communication Engineering, Faculty of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Centre of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
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20
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Abdallah T, Jrad N, Abdallah F, Humeau-Heurtier A, Van Bogaert P. A self-attention model for cross-subject seizure detection. Comput Biol Med 2023; 165:107427. [PMID: 37683531 DOI: 10.1016/j.compbiomed.2023.107427] [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: 05/12/2023] [Revised: 08/03/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
Epilepsy is a neurological disorder characterized by recurring seizures, detected by electroencephalography (EEG). EEG signals can be detected by manual time-consuming analysis and recently by automatic detection. The latter poses a significant challenge due to the high dimensional and non-stationary nature of EEG signals. Recently, deep learning (DL) techniques have emerged as valuable tools for seizure detection. In this study, a novel data-driven model based on DL, incorporating a self-attention mechanism (SAT), is proposed. One notable advantage of the proposed method is its simplicity in application, as the raw signal data is directly fed into the suggested network without requiring expertise in signal processing. The model leverages a one-dimensional convolutional neural network (CNN) to extract relevant features from EEG signals. These features are then passed through a long short-term memory (LSTM) module to benefit from its memory capabilities, along with a SAT mechanism. The key contribution of this paper lies in the addition of the SAT layer to the LSTM encoder, enabling enhanced exploration of the latent mapping during the encoding step. Cross-subject experiments revealed good performance of this approach with F1-score of 97.8% and 92.7% for binary and five-class epileptic seizure recognition tasks, respectively, on the public UCI dataset, and 97.9% on the CHB-MIT database, surpassing state-of-the-art DL performance. Besides, the proposed method exhibits robustness to inter-subject variability.
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Affiliation(s)
- Tala Abdallah
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France.
| | - Nisrine Jrad
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France; University of Catholique de l'Ouest, Angers-Nantes, 49000, France
| | | | - Anne Humeau-Heurtier
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France
| | - Patrick Van Bogaert
- Univ Angers, LARIS, SFR MATHSTIC, F-49000 Angers, 62 avenue Notre-Dame du Lac, France; The Department of Pediatric Neurology, CHU, Angers, 49000, France
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21
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Zheng S, Zhang X, Song P, Hu Y, Gong X, Peng X. Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages. Front Comput Neurosci 2023; 17:1211096. [PMID: 37841676 PMCID: PMC10570412 DOI: 10.3389/fncom.2023.1211096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky. Methods In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input. Results Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat. Discussion Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.
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Affiliation(s)
- Shiming Zheng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Xiaopei Zhang
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Panpan Song
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xi Gong
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
| | - Xiaoling Peng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China
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22
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Ilias L, Doukas G, Kontoulis M, Alexakis K, Michalitsi-Psarrou A, Ntanos C, Askounis D. Overview of methods and available tools used in complex brain disorders. OPEN RESEARCH EUROPE 2023; 3:152. [PMID: 38389699 PMCID: PMC10882203 DOI: 10.12688/openreseurope.16244.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 02/24/2024]
Abstract
Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually and in combination, increasing mortality risk, and contributing to the socioeconomic burden of patients, their families and, their communities at large. Although some literature reviews have been conducted mentioning the available methods and tools used for supporting the diagnosis of complex brain disorders and processing different files, there are still limitations. Specifically, these research works have focused primarily on one single brain disorder, i.e., sleep disorders or dementia or epilepsy. Additionally, existing research initiatives mentioning some tools, focus mainly on one single type of data, i.e., electroencephalography (EEG) signals or actigraphies or Magnetic Resonance Imaging, and so on. To tackle the aforementioned limitations, this is the first study conducting a comprehensive literature review of the available methods used for supporting the diagnosis of multiple complex brain disorders, i.e., Alzheimer's dementia, sleep disorders, epilepsy. Also, to the best of our knowledge, we present the first study conducting a comprehensive literature review of all the available tools, which can be exploited for processing multiple types of data, including EEG, actigraphies, and MRIs, and receiving valuable forms of information which can be used for differentiating people in a healthy control group and patients suffering from complex brain disorders. Additionally, the present study highlights both the benefits and limitations of the existing available tools.
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Affiliation(s)
- Loukas Ilias
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - George Doukas
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Michael Kontoulis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Konstantinos Alexakis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Ariadni Michalitsi-Psarrou
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Christos Ntanos
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
| | - Dimitris Askounis
- Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, 15773, Greece
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23
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Segal G, Keidar N, Lotan RM, Romano Y, Herskovitz M, Yaniv Y. Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction. Front Neurosci 2023; 17:1184990. [PMID: 37790590 PMCID: PMC10543660 DOI: 10.3389/fnins.2023.1184990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Epilepsy is a neurological disease characterized by sudden, unprovoked seizures. The unexpected nature of epileptic seizures is a major component of the disease burden. Predicting seizure onset and alarming patients may allow timely intervention, which would improve clinical outcomes and patient quality of life. Currently, algorithms aiming to predict seizures suffer from a high false alarm rate, rendering them unsuitable for clinical use. Methods We adopted here a risk-controlling prediction calibration method called Learn then Test to reduce false alarm rates of seizure prediction. This method calibrates the output of a "black-box" model to meet a specified false alarm rate requirement. The method was initially validated on synthetic data and subsequently tested on publicly available electroencephalogram (EEG) records from 15 patients with epilepsy by calibrating the outputs of a deep learning model. Results and discussion Validation showed that the calibration method rigorously controlled the false alarm rate at a user-desired level after our adaptation. Real data testing showed an average of 92% reduction in the false alarm rate, at the cost of missing four of nine seizures of six patients. Better-performing prediction models combined with the proposed method may facilitate the clinical use of real-time seizure prediction systems.
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Affiliation(s)
- Galya Segal
- Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-Israel Institute of Technology (IIT), Haifa, Israel
- Faculty of Medicine, Technion-Israel Institute of Technology (IIT), Haifa, Israel
| | - Noam Keidar
- Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-Israel Institute of Technology (IIT), Haifa, Israel
| | - Roy Maor Lotan
- Computer Science Department, Technion-Israel Institute of Technology (IIT), Haifa, Israel
- Electrical and Computer Engineering Department, Technion-Israel Institute of Technology (IIT), Haifa, Israel
| | - Yaniv Romano
- Computer Science Department, Technion-Israel Institute of Technology (IIT), Haifa, Israel
- Electrical and Computer Engineering Department, Technion-Israel Institute of Technology (IIT), Haifa, Israel
| | - Moshe Herskovitz
- Faculty of Medicine, Technion-Israel Institute of Technology (IIT), Haifa, Israel
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Yael Yaniv
- Laboratory of Bioenergetic and Bioelectric Systems, Biomedical Engineering Faculty, Technion-Israel Institute of Technology (IIT), Haifa, Israel
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24
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Hag A, Al-Shargie F, Handayani D, Asadi H. Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters. Brain Sci 2023; 13:1340. [PMID: 37759941 PMCID: PMC10527440 DOI: 10.3390/brainsci13091340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.
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Affiliation(s)
- Ala Hag
- School of Computer Science & Engineering, Taylor’s University, Jalan Taylors, Subang Jaya 47500, Selangor, Malaysia;
| | - Fares Al-Shargie
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Dini Handayani
- Department of Electrical Engineering, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates;
| | - Houshyar Asadi
- Computer Science Department, KICT, International Islamic University Malaysia, Kuala Lumpur 53100, Selangor, Malaysia
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Wang J, Liang S, Zhang J, Wu Y, Zhang L, Gao R, He D, Shi CJR. EEG Signal Epilepsy Detection With a Weighted Neighbor Graph Representation and Two-Stream Graph-Based Framework. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3176-3187. [PMID: 37506006 DOI: 10.1109/tnsre.2023.3299839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Epilepsy is one of the most common neurological diseases. Clinically, epileptic seizure detection is usually performed by analyzing electroencephalography (EEG) signals. At present, deep learning models have been widely used for single-channel EEG signal epilepsy detection, but this method is difficult to explain the classification results. Researchers have attempted to solve interpretive problems by combining graph representation of EEG signals with graph neural network models. Recently, the combination of graph representations and graph neural network (GNN) models has been increasingly applied to single-channel epilepsy detection. By this methodology, the raw EEG signal is transformed to its graph representation, and a GNN model is used to learn latent features and classify whether the data indicates an epileptic seizure episode. However, existing methods are faced with two major challenges. First, existing graph representations tend to have high time complexity as they generally require each vertex to traverse all other vertices to construct a graph structure. Some of them also have high space complexity for being dense. Second, while separate graph representations can be derived from a single-channel EEG signal in both time and frequency domains, existing GNN models for epilepsy detection can learn from a single graph representation, which makes it hard to let the information from the two domains complement each other. For addressing these challenges, we propose a Weighted Neighbour Graph (WNG) representation for EEG signals. Reducing the redundant edges of the existing graph, WNG can be both time and space-efficient, and as informative as its less efficient counterparts. We then propose a two-stream graph-based framework to simultaneously learn features from WNG in both time and frequency domain. Extensive experiments demonstrate the effectiveness and efficiency of the proposed methods.
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Zeng Z, Xu Y, Zhou Y, Su R, Tao L, Wang Z, Chen C, Chen W. Prognostic Analysis of KCNQ2 Patients via Combining EEG Deep Features and Machine Learning Classifiers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083766 DOI: 10.1109/embc40787.2023.10341098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Pathogenic variants of the KCNQ2 gene often induces neonatal epilepsy in clinical. For better treatment, infants with confirmed KCNQ2 pathogenic variant and epilepsy symptoms need to adjust their treatment plans according to the outcome after taking antiseizure medicines (ASMs). This process is often time-consuming and requires long-term follow-up, which undoubtedly causes unnecessary psychological and economic burdens. In this study, we investigate the feasibility to predict the outcome of KCNQ2 patients via Electroencephalogram (EEG). By using the combination of deep networks and classical classifiers, the abnormal brain pathological activities recorded in EEGs can be encoded into deep features and decoded into specific KCNQ2 outcomes, thus taking the advantage of both powerful feature extraction capability from deep networks and stronger classification ability from classical classifiers. Specifically, we acquire 10-channel EEG signals from 33 infants with KCNQ2 pathogenic variants after taking ASMs. Two well-trained models (Resnet-50 and Resnet-18) are employed to extract deep features from the EEG spectrums. We achieve an accuracy of 78.7% to predict the KCNQ2 outcome of each infant. To our best knowledge, this is the first study to employ potential EEG pathological differences to predict the outcomes of KCNQ2 patients. The investigation of automatic KCNQ2 outcome prediction may contribute to a more convenient diagnosis mechanism for KCNQ2 patients.
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Argo A, Puntarello M, Malta G, Buscemi R, Scalzo G, Triolo V, Albano GD, Zerbo S. The analysis of SUDEP forensic autopsies leading to preventable events. Front Neurol 2023; 14:1231515. [PMID: 37456625 PMCID: PMC10346851 DOI: 10.3389/fneur.2023.1231515] [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: 05/30/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction The diagnosis of unexpected death by excluding non-natural causes, particularly in subjects with epilepsy, is a topic of interest and it is difficult to identify in the forensic field. Health professionals sometimes are faced with cases of sudden death, generally in young adults with a long history of epilepsy that require, for judicial purposes, an explanation in terms of cause and means to determine the death. SUDEP is an entity diagnosed by the exclusion of other causes that may have led to death, and then for forensic purposes, it requires particular attention and knowledge, and there is difficulty in identifying it. Our contribution aims to illustrate the scientific community pathological findings, medical history, and circumstantial evidence of four cases of sudden death in epileptic subjects. Method We illustrated four cases of judicial autopsies from the Institute of Forensic Medicine of Palermo, Italy; the purpose was to exclude the criminal intervention in determining the death as non-natural. The study of victims' medical history, the toxicological investigations, and the autopsy findings analyzed both from macroscopic and microscopic aspects have made it possible to highlight some findings that can be traced back to SUDEP despite the small sample of subjects studied. Results These presented findings of four SUDEP cases could help forensic pathologists in recognizing this entity, by highlighting its characteristics, and allowing for a pathological classification, also in relation to the use of drugs for epilepsy treatment and circumstances of death. Discussion To obtain a definite diagnosis of SUDEP, a complex investigation process is required in a multidisciplinary approach. Considering the literature review with criticism, it could allow health professionals to select the characteristics of epileptic patients at risk of sudden death. Processing human behaviors, molecular and histopathological findings of the autopsies, but also the physiological, and pathological human body system functions thanks to Artificial Intelligence, could be the key to explaining SUDEP mechanisms and the future results to prevent it.
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Affiliation(s)
- Antonina Argo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | - Maria Puntarello
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | - Ginevra Malta
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | - Roberto Buscemi
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | - Giovanni Scalzo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | | | - Giuseppe Davide Albano
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
| | - Stefania Zerbo
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Section of Legal Medicine, University of Palermo, Palermo, Italy
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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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Chen H, Wang Z, Lu C, Shu F, Chen C, Wang L, Chen W. Neonatal Seizure Detection Using a Wearable Multi-Sensor System. Bioengineering (Basel) 2023; 10:658. [PMID: 37370589 DOI: 10.3390/bioengineering10060658] [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: 03/02/2023] [Revised: 04/27/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.
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Affiliation(s)
- Hongyu Chen
- Greater Bay Area Institute of Precision Medicine, Guangzhou 511466, China
| | - Zaihao Wang
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Chunmei Lu
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Feng Shu
- Collaborative Innovation Center of Polymers and Polymer Composites, Department of Macromolecular Science, Fudan University, Shanghai 201203, China
| | - Chen Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Laishuan Wang
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, 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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Bhagubai M, Vandecasteele K, Swinnen L, Macea J, Chatzichristos C, De Vos M, Van Paesschen W. The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG. Bioengineering (Basel) 2023; 10:bioengineering10040491. [PMID: 37106678 PMCID: PMC10136326 DOI: 10.3390/bioengineering10040491] [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: 02/27/2023] [Revised: 04/07/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm's detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.
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Affiliation(s)
- Miguel Bhagubai
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Kaat Vandecasteele
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Lauren Swinnen
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
| | - Jaiver Macea
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
| | - Christos Chatzichristos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, 3001 Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, University Hospital Leuven, 3000 Leuven, Belgium
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32
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Gascoigne SJ, Waldmann L, Schroeder GM, Panagiotopoulou M, Blickwedel J, Chowdhury F, Cronie A, Diehl B, Duncan JS, Falconer J, Faulder R, Guan Y, Leach V, Livingstone S, Papasavvas C, Thomas RH, Wilson K, Taylor PN, Wang Y. A library of quantitative markers of seizure severity. Epilepsia 2023; 64:1074-1086. [PMID: 36727552 PMCID: PMC10952709 DOI: 10.1111/epi.17525] [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: 08/02/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches to electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures. METHODS We analyzed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and postictal suppression of seizures. RESULTS Quantitative EEG markers of seizure severity distinguished focal versus subclinical seizures across patients. In individual patients, 53% had a moderate to large difference (rank sumr > .3 ,p < .05 ) between focal and subclinical seizures in three or more markers. Circadian and longer term changes in severity were found for the majority of patients. SIGNIFICANCE We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.
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Affiliation(s)
- Sarah J. Gascoigne
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Gabrielle M. Schroeder
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Mariella Panagiotopoulou
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Jess Blickwedel
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | | | - Beate Diehl
- UCL Queen Square Institute of NeurologyLondonUK
| | | | | | - Ryan Faulder
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Yu Guan
- Department of Computer ScienceUniversity of WarwickWarwickUK
| | | | | | - Christoforos Papasavvas
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Kevin Wilson
- School of Mathematics, Statistics, and PhysicsNewcastle UniversityNewcastle Upon TyneUK
| | - Peter N. Taylor
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle Upon TyneUK
| | - Yujiang Wang
- Computational Neurology, Neuroscience & Psychiatry Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
- UCL Queen Square Institute of NeurologyLondonUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle Upon TyneUK
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Galan-Uribe E, Amezquita-Sanchez JP, Morales-Velazquez L. Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators. SENSORS (BASEL, SWITZERLAND) 2023; 23:3213. [PMID: 36991923 PMCID: PMC10054698 DOI: 10.3390/s23063213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.
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Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges. Diagnostics (Basel) 2023; 13:diagnostics13061058. [PMID: 36980366 PMCID: PMC10047386 DOI: 10.3390/diagnostics13061058] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 03/14/2023] Open
Abstract
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
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35
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Bhuvaneshwari M, Grace Mary Kanaga E, Anitha J. Bio-inspired Red Fox-Sine cosine optimization for the feature selection of SSVEP-based EEG signals for BCI applications. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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36
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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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Singh AK, Krishnan S. Trends in EEG signal feature extraction applications. Front Artif Intell 2023; 5:1072801. [PMID: 36760718 PMCID: PMC9905640 DOI: 10.3389/frai.2022.1072801] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/28/2022] [Indexed: 01/26/2023] Open
Abstract
This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.
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Visalini K, Alagarsamy S, Nagarajan D. Neonatal seizure detection using deep belief networks from multichannel EEG data. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08254-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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39
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Goel S, Agrawal R, Bharti R. Epileptic seizure prediction and classification based on statistical features using LSTM fully connected neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Epilepsy, the most common neurological disorder by which over 65 million people are affected across the world. Recent research has shown a very large interest to predict and diagnose epilepsy well before time. The continuous monitoring of EEG signals for seizure detection in electroencephalogram (EEG) is a very tedious and time taking process and therefore requires a qualified and trained clinical specialist. This paper presents a novel approach to detect and predict the epileptic signal in the recorded electroencephalogram (EEG). There is always a requirement for a nonlinear technique to examine the EEG signals due to the random nature of EEG signals. Therefore, we are providing an alternate method that extracts various entropy measures such Sample Entropy, Spectral Entropy, Permutation Entropy, and Shannon Entropy as statistical features from EEG signal. Based on these extracted features LSTM Fully connected Neural Network is used to classify the EEG signal as Focal and Non-focal. The proposed method gives a new insight into EEG signals by providing sensitivity as an added measure using deep learning along with accuracy and precision.
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Affiliation(s)
- Sachin Goel
- Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun, India
| | - Rajeev Agrawal
- Lloyd Institute of Engineering & Technology, Greater Noida, India
| | - R.K. Bharti
- Bipin Tripathi Kumaon Institute of Technology, Dwarahat, Uttarakhand, India
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Mousa KM, Mousa FA, Mohamed HS, Elsawy MM. Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt. SAGE Open Nurs 2023; 9:23779608231185873. [PMID: 37435577 PMCID: PMC10331222 DOI: 10.1177/23779608231185873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/10/2023] [Accepted: 06/15/2023] [Indexed: 07/13/2023] Open
Abstract
Introduction In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation. Objective The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers. Methods A case-control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study. Results The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers. Conclusion Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.
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Affiliation(s)
- Khadraa Mohamed Mousa
- Community Health Nursing Department, Faculty of Nursing, Cairo University, Cairo, Egypt
| | - Farid Ali Mousa
- Information Technology Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Helalia Shalabi Mohamed
- Community Health Nursing Department, Faculty of Nursing, Cairo University, Cairo, Egypt
- Community Health Nursing, College of Nursing, PAAET, Safat, Kuwait
| | - Manal Mohamed Elsawy
- Community Health Nursing Department, Faculty of Nursing, Cairo University, Cairo, Egypt
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41
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Nhu D, Janmohamed M, Shakhatreh L, Gonen O, Perucca P, Gilligan A, Kwan P, O'Brien TJ, Tan CW, Kuhlmann L. Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches. Int J Neural Syst 2023; 33:2350001. [PMID: 36599664 DOI: 10.1142/s0129065723500016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing works viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these methods were evaluated on any public datasets, making direct comparisons challenging. This paper explored two state-of-the-art convolutional-based TSC algorithms, InceptionTime and Minirocket, on IED detection. We fine-tuned and cross-evaluated them on a public (Temple University Events - TUEV) and two private datasets and provided ready metrics for benchmarking future work. We observed that the optimal parameters correlated with the clinical duration of an IED and achieved the best area under precision-recall curve (AUPRC) of 0.98 and F1 of 0.80 on the private datasets, respectively. The AUPRC and F1 on the TUEV dataset were 0.99 and 0.97, respectively. While algorithms trained on the private sets maintained their performance when tested on the TUEV data, those trained on TUEV could not generalize well to the private data. These results emerge from differences in the class distributions across datasets and indicate a need for public datasets with a better diversity of IED waveforms, background activities and artifacts to facilitate standardization and benchmarking of algorithms.
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Affiliation(s)
- D Nhu
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - M Janmohamed
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - L Shakhatreh
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - O Gonen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - P Perucca
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia.,Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, VIC, Australia.,Epilepsy Research Center, Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| | - A Gilligan
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, VIC, Australia.,Neurosciences Clinical Institute, Epworth Healthcare, Melbourne, VIC, Australia
| | - P Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - T J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - C W Tan
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
| | - L Kuhlmann
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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42
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A novel feature extraction method using chemosensory EEG for Parkinson's disease classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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43
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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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44
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Einizade A, Nasiri S, Mozafari M, Sardouie SH, Clifford GD. Explainable automated seizure detection using attentive deep multi-view networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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45
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Amiri M, Aghaeinia H, Amindavar HR. Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing transform. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104022] [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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46
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Gu B, Adeli H. Toward automated prediction of sudden unexpected death in epilepsy. Rev Neurosci 2022; 33:877-887. [PMID: 35619127 DOI: 10.1515/revneuro-2022-0024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/19/2022] [Indexed: 12/14/2022]
Abstract
Sudden unexpected death in epilepsy (SUDEP) is a devastating yet overlooked complication of epilepsy. The rare and complex nature of SUDEP makes it challenging to study. No prediction or prevention of SUDEP is currently available in a clinical setting. In the past decade, significant advances have been made in our knowledge of the pathophysiologic cascades that lead to SUDEP. In particular, studies of brain, heart, and respiratory functions in both human patients at the epilepsy monitoring unit and animal models during fatal seizures provide critical information to integrate computational tools for SUDEP prediction. The rapid advances in automated seizure detection and prediction algorithms provide a fundamental framework for their adaption in predicting SUDEP. If a SUDEP can be predicted, then there will be a potential for medical intervention to be administered, either by their caregivers or via an implanted device automatically delivering electrical stimulation or medication, and finally save lives from fatal seizures. This article presents recent developments of SUDEP studies focusing on the pathophysiologic basis of SUDEP and computational implications of machine learning techniques that can be adapted and extended for SUDEP prediction. This article also discusses some novel ideas for SUDEP prediction and rescue including principal component analysis and closed-loop intervention.
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Affiliation(s)
- Bin Gu
- Department of Neuroscience, Ohio State University, Columbus, OH 43210, USA
| | - Hojjat Adeli
- Department of Neuroscience, Ohio State University, Columbus, OH 43210, USA.,Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA
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47
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Zambrana-Vinaroz D, Vicente-Samper JM, Manrique-Cordoba J, Sabater-Navarro JM. Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:9372. [PMID: 36502071 PMCID: PMC9736525 DOI: 10.3390/s22239372] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/26/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
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Affiliation(s)
- David Zambrana-Vinaroz
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Jose Maria Vicente-Samper
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Juliana Manrique-Cordoba
- Neuroengineering Biomedical Research Group, Miguel Hernández University of Elche, 03202 Elche, Spain
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48
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Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity. Sci Rep 2022; 12:18998. [PMID: 36348082 PMCID: PMC9643358 DOI: 10.1038/s41598-022-23656-1] [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: 08/15/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.
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49
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Zhao F, Gao T, Cao Z, Chen X, Mao Y, Mao N, Ren Y. Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal. Front Comput Neurosci 2022; 16:1046310. [PMID: 36387303 PMCID: PMC9647659 DOI: 10.3389/fncom.2022.1046310] [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: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/03/2023] Open
Abstract
Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Tianyu Gao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Zhi Cao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
- College of Oceanography and Space Informatics, China University of Petroleum (Huadong), Qingdao, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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50
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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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