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Kantipudi MVVP, Kumar NSP, Aluvalu R, Selvarajan S, Kotecha K. An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection. Sci Rep 2024; 14:843. [PMID: 38191643 PMCID: PMC10774431 DOI: 10.1038/s41598-024-51337-8] [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: 09/08/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024] Open
Abstract
Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.
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Affiliation(s)
- M V V Prasad Kantipudi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, 412115, India
| | - N S Pradeep Kumar
- S.E.A College of Engineering and Technology, Bengaluru, 560049, India
| | - Rajanikanth Aluvalu
- Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
- Department of Computer Science, Kebri Dehar University, Somali, Ethiopia.
| | - K Kotecha
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, 412115, India
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed) University, Pune, 412115, India
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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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Liu S, Wang J, Li S, Cai L. Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3884-3894. [PMID: 37725738 DOI: 10.1109/tnsre.2023.3317093] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction on two public datasets. The average classification accuracy of the periodic and aperiodic components for 10 clinical tasks on the Bonn EEG database were 73.9% and 96.68%, respectively, and increases to 98.88% when combined. For 22 patients on the CHB-MIT Long-term EEG database, the combined features achieve an average detection accuracy of 99.95% and successfully predict all seizures with low false prediction rates. We conclude that both the periodic and aperiodic components of the EEG power spectrum contributed to discriminating different stages of epilepsy, but the aperiodic neural activity played a decisive role in classification. This discovery has significant implications for diagnosing epileptic seizures and providing personalized brain activity information to improve the accuracy and efficiency of epilepsy detection.
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Nandini D, Yadav J, Rani A, Singh V. Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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Zeng W, Shan L, Su B, Du S. Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers. Front Neurosci 2023; 17:1145526. [PMID: 37284662 PMCID: PMC10239853 DOI: 10.3389/fnins.2023.1145526] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/02/2023] [Indexed: 06/08/2023] Open
Abstract
Introduction In the clinical setting, it becomes increasingly important to detect epileptic seizures automatically since it could significantly reduce the burden for the care of patients suffering from intractable epilepsy. Electroencephalography (EEG) signals record the brain's electrical activity and contain rich information about brain dysfunction. As a non-invasive and inexpensive tool for detecting epileptic seizures, visual evaluation of EEG recordings is labor-intensive and subjective and requires significant improvement. Methods This study aims to develop a new approach to recognize seizures automatically using EEG recordings. During feature extraction of EEG input from raw data, we construct a new deep neural network (DNN) model. Deep feature maps derived from layers placed hierarchically in a convolution neural network are put into different kinds of shallow classifiers to detect the anomaly. Feature maps are reduced in dimensionality using Principal Component Analysis (PCA). Results By analyzing the EEG Epilepsy dataset and the Bonn dataset for epilepsy, we conclude that our proposed method is both effective and robust. These datasets vary significantly in the acquisition of data, the formulation of clinical protocols, and the storage of digital information, making processing and analysis challenging. On both datasets, extensive experiments are performed using a cross-validation by 10 folds strategy to demonstrate approximately 100% accuracy for binary and multi-category classification. Discussion In addition to demonstrating that our methodology outperforms other up-to-date approaches, the results of this study also suggest that it can be applied in clinical practice as well.
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Affiliation(s)
- Wei Zeng
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, China
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
| | - Liangmin Shan
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, China
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
| | - Bo Su
- School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, China
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
| | - Shaoyi Du
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
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Nafea MS, Ismail ZH. Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review. Bioengineering (Basel) 2022; 9:781. [PMID: 36550987 PMCID: PMC9774931 DOI: 10.3390/bioengineering9120781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/13/2022] Open
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
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Affiliation(s)
- Mohamed Sami Nafea
- Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Cairo 2033, Egypt
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
| | - Zool Hilmi Ismail
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
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Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9579422. [PMID: 36483658 PMCID: PMC9726261 DOI: 10.1155/2022/9579422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/24/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022]
Abstract
Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets.
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Nemati N, Meshgini S. A medium-weight deep convolutional neural network-based approach for onset epileptic seizures classification in EEG signals. Brain Behav 2022; 12:e2763. [PMID: 36196623 PMCID: PMC9660412 DOI: 10.1002/brb3.2763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 12/07/2021] [Accepted: 01/11/2022] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long-term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover underlying causes, and avoid applicable risk factors. Progress in diagnosing onset epileptic seizures can ensure that seizures and destroyed damages are detectable at the time of manifestation. Previous seizure detection models had problems with the presence of multiple features, the lack of an appropriate signal descriptor, and the time-consuming analysis, all of which led to uncertainty and different interpretations. Deep learning has recently made tremendous progress in categorizing and detecting epilepsy. METHOD This work proposes an effective classification strategy in response to these issues. The discrete wavelet transform (DWT) is used to breakdown the EEG signal, and a deep convolutional neural network (DCNN) is used to diagnose epileptic seizures in the first phase. Using a medium-weight DCNN (mw-DCNN) architecture, we use a preprocess phase to improve the decision-maker method. The proposed approach was tested on the CHEG-MIT Scalp EEG database's collected EEG signals. RESULT The results of the studies reveal that the mw-DCNN algorithm produces proper classification results under various conditions. To solve the uncertainty challenge, K-fold cross-validation was used to assess the algorithm's repeatability at the test level, and the accuracies were evaluated in the range of 99%-100%. CONCLUSION The suggested structure can assist medical specialistsin analyzing epileptic seizures' EEG signals more precisely.
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Affiliation(s)
- Nazanin Nemati
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
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9
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Sun W, Wang X, Tan B. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:49684-49699. [PMID: 35220530 DOI: 10.1007/s11356-022-19388-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Accurate wind speed forecasting (WSF) not only ensures stable power system operation but also contributes to enhancing the competitiveness of wind power companies in the market. In this paper, a hybrid prediction model based on secondary decomposition algorithm (SDA) is proposed for WSF. First, wavelet transform (WT) is used to decompose the wind speed sequence into approximate and detailed components. Second, the obtained detailed components are further decomposed by symplectic geometry mode decomposition (SGMD). Then, the marine predators algorithm-optimized back-propagation neural network (BPNN) is used to predict the new subsequences. The case study was implemented on 4 datasets. The experimental results show that, first, the proposed hybrid model has the highest prediction accuracy and the best robustness among all the compared models in 1-4-step prediction. Second, the proposed hybrid decomposition strategy has significant utility in reducing the difficulty of WSF. After adding SDA, the average improvement levels of MAPE in 1-4-step prediction were 85.64%, 84.93%, 81.08% and 80.67%, respectively. Third, the re-decomposition of the details obtained by WT can improve the prediction accuracy. After the re-decomposition of the details obtained by WT, the proposed WT-SGMD-MPA-BP model leads to the average improvement percentages of 44.44%, 61.69%, 50.56% and 49.28% in RMSE compared with WT-MPA-BP model in various horizons. The proposed model provides valuable reference for WSF. In future work, the performance of the model for other nonlinear sequences is worth exploring.
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Affiliation(s)
- Wei Sun
- Economics and Management Department, North China Electric Power University, Baoding, 071000, Hebei, China
| | - Xiaoxuan Wang
- Economics and Management Department, North China Electric Power University, Baoding, 071000, Hebei, China.
| | - Bin Tan
- Economics and Management Department, North China Electric Power University, Baoding, 071000, Hebei, China
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10
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An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications.
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Guo Y, Jiang X, Tao L, Meng L, Dai C, Long X, Wan F, Zhang Y, van Dijk J, Aarts RM, Chen W, Chen C. Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble. IEEE Trans Neural Syst Rehabil Eng 2022; 30:915-924. [PMID: 35353703 DOI: 10.1109/tnsre.2022.3163503] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.
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12
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An Adaptive Classification Model for Predicting Epileptic Seizures Using Cloud Computing Service Architecture. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073408] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed labels consider missing values, and the Model-based imputation method is used to handle them in the global classifier. Implementing the proposed model on a real huge dataset (CHB-MIT) showed an accurate performance. It has a 0.998 True Negative Rate, a 0.785 True Positive Rate, and a 0.0017 False Positive Rate, which overcomes much of the research in the state-of-the-art.
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13
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Multi-step wind speed forecasting based on secondary decomposition algorithm and optimized back propagation neural network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107894] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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14
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Sukriti, Chakraborty M, Mitra D. A computationally efficient automated seizure detection method based on the novel idea of multiscale spectral features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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15
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Synchroextracting chirplet transform-based epileptic seizures detection using EEG. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Peng H, Lei C, Zheng S, Zhao C, Wu C, Sun J, Hu B. Automatic epileptic seizure detection via Stein kernel-based sparse representation. Comput Biol Med 2021; 132:104338. [PMID: 33780870 DOI: 10.1016/j.compbiomed.2021.104338] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/20/2021] [Accepted: 03/10/2021] [Indexed: 12/29/2022]
Abstract
Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This paper presents a novel method for seizure detection using the Stein kernel-based sparse representation (SR) for EEG recordings. Different from the traditional SR scheme that works with vector data in Euclidean space, the Stein kernel-based SR framework is constructed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Due to the non-Euclidean geometry of the Riemannian manifold, the Stein kernel on the manifold permits the embedding of the manifold in a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. In the Stein kernel-based SR framework, EEG samples are described by SPD matrices in the form of covariance descriptors (CovDs). Then, a test EEG sample is sparsely represented on the training set, and the test sample is classified as a member of the class, which leads to the minimum reconstructed residual. Finally, by using three widely used EEG datasets to evaluate the detection performance of the proposed method, the experimental results demonstrate that it achieves good classification accuracy on each dataset. Furthermore, the fast computational speed of the Stein kernel-based SR also meets the basic requirements for real-time seizure detection.
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Affiliation(s)
- Hong Peng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Chang Lei
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Shuzhen Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Chengjian Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Chunyun Wu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Jieqiong Sun
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China.
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17
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Zhou D, Li X. Epilepsy EEG Signal Classification Algorithm Based on Improved RBF. Front Neurosci 2020; 14:606. [PMID: 32655355 PMCID: PMC7324866 DOI: 10.3389/fnins.2020.00606] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 05/18/2020] [Indexed: 11/24/2022] Open
Abstract
Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.
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Affiliation(s)
- Dongmei Zhou
- College of Information Science and Technology, Chengdu University of Technology, Chengdu, China
| | - Xuemei Li
- Xijing Hospital, Air Force Medical University, Xi'an, China
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