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Ma H, Wu Y, Tang Y, Chen R, Xu T, Zhang W. Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction. Comput Biol Med 2024; 176:108565. [PMID: 38744007 DOI: 10.1016/j.compbiomed.2024.108565] [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: 12/22/2023] [Revised: 04/09/2024] [Accepted: 05/05/2024] [Indexed: 05/16/2024]
Abstract
Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.
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Affiliation(s)
- Hongcheng Ma
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yajing Wu
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yongqiang Tang
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Rui Chen
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Tao Xu
- Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, China.
| | - Wensheng Zhang
- School of Information and Communication Engineering, Hainan University, Haikou, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China.
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2
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Li X, Qiao Y, Duan L, Miao J. Epilepsy EEG signals classification based on sparse principal component logistic regression model. Comput Methods Biomech Biomed Engin 2024:1-9. [PMID: 38424732 DOI: 10.1080/10255842.2024.2321991] [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: 09/05/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Epilepsy is a chronic brain disease caused by excessive discharge of brain neurons. Long-term recurrent seizures bring a lot of trouble to patients and their families. Prediction of different stages of epilepsy is of great significance. We extract pearson correlation coefficients (PCC) between channels in different frequency bands as features of EEG signals for epilepsy stages prediction. However, the features are of large feature dimension and serious multi-collinearity. To eliminate these adverse influence, the combination of traditional dimension reduction method principal component analysis (PCA) and logistic regression method with regularization term is proposed to avoid over-fitting and achieve the feature sparsity. The experiments are conducted on the widely used CHB-MIT dataset using different regularization terms L 1 and L 2 , respectively. The proposed method identifies various stages of epilepsy quickly and efficiently, and it presents the best average accuracy of 94.86%, average precision of 96.71%, average recall of 93.48%, average kappa value of 0.90 and average Matthews correlation coefficient (MCC) value of 0.90 for all patients.
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Affiliation(s)
- Xi Li
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China
| | - Yuanhua Qiao
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China
| | - Lijuan Duan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jun Miao
- School of Computer Science, Beijing Information Science and Technology University, Beijing, China
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3
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Ji H, Xu T, Xue T, Xu T, Yan Z, Liu Y, Chen B, Jiang W. An effective fusion model for seizure prediction: GAMRNN. Front Neurosci 2023; 17:1246995. [PMID: 37674519 PMCID: PMC10477703 DOI: 10.3389/fnins.2023.1246995] [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: 06/25/2023] [Accepted: 07/24/2023] [Indexed: 09/08/2023] Open
Abstract
The early prediction of epileptic seizures holds paramount significance in patient care and medical research. Extracting useful spatial-temporal features to facilitate seizure prediction represents a primary challenge in this field. This study proposes GAMRNN, a novel methodology integrating a dual-layer gated recurrent unit (GRU) model with a convolutional attention module. GAMRNN aims to capture intricate spatial-temporal characteristics by highlighting informative feature channels and spatial pattern dynamics. We employ the Lion optimization algorithm to enhance the model's generalization capability and predictive accuracy. Our evaluation of GAMRNN on the widely utilized CHB-MIT EEG dataset demonstrates its effectiveness in seizure prediction. The results include an impressive average classification accuracy of 91.73%, sensitivity of 88.09%, specificity of 92.09%, and a low false positive rate of 0.053/h. Notably, GAMRNN enables early seizure prediction with a lead time ranging from 5 to 35 min, exhibiting remarkable performance improvements compared to similar prediction models.
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Affiliation(s)
- Hong Ji
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Ting Xu
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xue
- Shaanxi Provincial Key Laboratory of Fashion Design Intelligence, Xi'an Polytechnic University, Xi'an, China
| | - Tao Xu
- School of Software, Northwestern Polytechnical University, Xi'an, China
| | - Zhiqiang Yan
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yonghong Liu
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Badong Chen
- Institute of Artistic Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Wen Jiang
- Xijing Hospital, Fourth Military Medical University, Xi'an, China
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4
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Wu X, Yang Z, Zhang T, Zhang L, Qiao L. An end-to-end seizure prediction approach using long short-term memory network. Front Hum Neurosci 2023; 17:1187794. [PMID: 37275341 PMCID: PMC10232837 DOI: 10.3389/fnhum.2023.1187794] [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: 03/16/2023] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children's Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.
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Affiliation(s)
- Xiao Wu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Zhaohui Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Tinglin Zhang
- School of Information, Yancheng Institute of Technology, Yancheng, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Lishan Qiao
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
- School of Mathematics Science, Liaocheng University, Liaocheng, China
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5
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Effective Epileptic Seizure Detection by Classifying Focal and Non-focal EEG Signals using Human Learning Optimization-based Hidden Markov Model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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6
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Assali I, Ghazi Blaiech A, Ben Abdallah A, Ben Khalifa K, Carrère M, Hédi Bedoui M. CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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7
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Patient-specific method for predicting epileptic seizures based on DRSN-GRU. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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8
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Ren Z, Han X, Wang B. The performance evaluation of the state-of-the-art EEG-based seizure prediction models. Front Neurol 2022; 13:1016224. [PMID: 36504642 PMCID: PMC9732735 DOI: 10.3389/fneur.2022.1016224] [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: 08/10/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
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Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Xiong Han
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China,*Correspondence: Xiong Han
| | - Bin Wang
- Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, China,Department of Neurology, Henan Provincial People's Hospital, Zhengzhou, China
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9
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Classification of EEG Signals for Prediction of Epileptic Seizures. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.
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10
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El-Gindy SAE, Ibrahim FE, Alabasy M, Abdelzaher HM, El-Refy M, Khalaf AAM, El-Dolil SM, El-Fishawy AS, Taha TE, El-Rabaie ESM, Dessouky MI, El-Dokany I, Oraby OA, N. Alotaiby T, Alshebeili SA, Abd El-Samie FE. Detection of Abnormal Activities from Various Signals Based on Statistical Analysis. WIRELESS PERSONAL COMMUNICATIONS 2022; 125:1013-1046. [DOI: 10.1007/s11277-022-09565-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/29/2022] [Indexed: 09/02/2023]
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11
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Multi-Channel Vision Transformer for Epileptic Seizure Prediction. Biomedicines 2022; 10:biomedicines10071551. [PMID: 35884859 PMCID: PMC9312955 DOI: 10.3390/biomedicines10071551] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 02/04/2023] Open
Abstract
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28-91.15% for invasive EEG data.
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12
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E P Moghaddam D, Sheth S, Haneef Z, Gavvala J, Aazhang B. Epileptic seizure prediction using spectral width of the covariance matrix. J Neural Eng 2022; 19. [PMID: 35320787 DOI: 10.1088/1741-2552/ac6063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
Epilepsy is a common neurological disorder in which patients suffer from sudden and unpredictable seizures. Seizures are caused by excessive and abnormal neuronal activity. Different methods have been employed to investigate electroencephalogram (EEG) data in patients with epilepsy. This paper introduces a simple yet accurate array-based method to study and predict seizures. We use the CHB-MIT dataset (all 24 cases), which includes scalp EEG recordings. The proposed method is based on the random matrix theory. After applying wavelet decomposition to denoise the data, we analyze the spatial coherence of the epileptic recordings by looking at the width of the covariance matrix eigenvalue distribution at different time and frequency bins. We train patient-specific support vector machine (SVM) classifiers to distinguish between interictal and preictal data with high performance and a false prediction rate as low as 0.09/h. The proposed technique achieves an average accuracy, specificity, sensitivity, and area under the curve (AUC) of 99.05%, 93.56%, 99.09%, and 0.99, respectively. Our proposed method outperforms state-of-the-art works in terms of sensitivity while maintaining a low false prediction rate. Also, in contrast to neural networks, which may achieve high performance, this work provides high sensitivity without compromising interpretability.
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Affiliation(s)
- Dorsa E P Moghaddam
- Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX 77005, Houston, Texas, 77005, UNITED STATES
| | - Sameer Sheth
- Neurosurgery, Baylor College of Medicine, 7200 Cambridge, Houston, Texas, 77005, UNITED STATES
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, Houston, Texas, 77030, UNITED STATES
| | - Jay Gavvala
- Neurology-Neurophysiology, Baylor College of Medicine, Baylor College of Medicine Medical Center, McNair Campus, 7200 Cambridge St., 9th Floor, MS: BCM609 Houston, TX 77030, Houston, Texas, 77030 , UNITED STATES
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, George R. Brown School of Engineering, 6100 Main Street, Houston, TX 77005, USA, Houston, 77005, UNITED STATES
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13
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El-Gindy SAE, Hamad A, El-Shafai W, Khalaf AAM, El-Dolil SM, Taha TE, El-Fishawy AS, Alotaiby TN, Alshebeili SA, El-Samie FEA. Efficient communication and EEG signal classification in wavelet domain for epilepsy patients. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:9193-9208. [DOI: 10.1007/s12652-020-02624-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 10/20/2020] [Indexed: 09/01/2023]
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14
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Patient-specific method of sleep electroencephalography using wavelet packet transform and Bi-LSTM for epileptic seizure prediction. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102963] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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15
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Muhammad Usman S, Khalid S, Bashir S. A deep learning based ensemble learning method for epileptic seizure prediction. Comput Biol Med 2021; 136:104710. [PMID: 34364257 DOI: 10.1016/j.compbiomed.2021.104710] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/29/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022]
Abstract
In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.
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Affiliation(s)
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria University, Islamabad, Pakistan.
| | - Sadaf Bashir
- Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
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16
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Assali I, Jlassi I, Aissi M, Blaiech AG, Carrère M, Bedoui MH. Comparison by multivariate auto-regressive method of seizure prediction for real patients and virtual patients. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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17
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Rout SK, Sahani M, Dash PK, Biswal PK. Multifuse multilayer multikernel RVFLN+ of process modes decomposition and approximate entropy data from iEEG/sEEG signals for epileptic seizure recognition. Comput Biol Med 2021; 132:104299. [PMID: 33711557 DOI: 10.1016/j.compbiomed.2021.104299] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/23/2021] [Accepted: 02/24/2021] [Indexed: 12/01/2022]
Abstract
In this paper, the extracted features using variational mode decomposition (VMD) and approximate entropy (ApEn) privileged information of the input EEG signals are combined with multilayer multikernel random vector functional link network plus (MMRVFLN+) classifier to recognize the epileptic seizure epochs efficaciously. In our experiment Bonn University single-channel intracranial electroencephalogram (iEEG) and Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multichannel scalp EEG (sEEG) recordings are considered to evaluate the efficacy of the proposed method. The VMD is applied on chaotic, non-stationary, nonlinear, and complex EEG signal to decompose it into three band-limited intrinsic mode functions (BLIMFs). The Hilbert transform (HT) is applied on BLIMFs to extract informative spectral and temporal features. The ApEn is computed from the raw EEG signals as the privileged information and given to the multi-hidden layer structure to obtain the most discriminative compressed form. The scatter plots show the distinct nature of compressed privileged ApEn information among the seizure pattern classes. The linear as well as nonlinear mapping, local and global kernel function, high-learning speed, less computationally complex MMRVFLN+ classifier is proposed to recognize the seizure events accurately by importing the efficacious features with ApEn as the input. The advanced signal processing algorithm i.e., Hilbert Huang transform (HHT) with ApEn and MMRVFLN+ are combined to compare the performance with the proposed VMDHTApEn-MMRVFLN+ method. The proposed method has remarkable recognition ability, superior classification accuracy, and excellent overall performance as compared to other methods. The digital architecture of the multifuse MMRVFLN+ is developed and implemented on a high-speed reconfigurable FPGA hardware platform to validate the effectiveness of the proposed method. The superior classification accuracy, the negligible false positive rate per hour (FPR/h), simplicity, feasibility, robustness, and practicability of the proposed method validate its ability to recognize the epileptic seizure epochs automatically.
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Affiliation(s)
- Susanta Kumar Rout
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India; International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Mrutyunjaya Sahani
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India.
| | - P K Dash
- Siksha 'O' Anusandhan Deemed to Be University, Bhubaneswar, Odisha, India
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18
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Usman SM, Khalid S, Bashir Z. Epileptic seizure prediction using scalp electroencephalogram signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.01.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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