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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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Liu C, Chen W, Li M. A hybrid EEG classification model using layered cascade deep learning architecture. Med Biol Eng Comput 2024; 62:2213-2229. [PMID: 38507121 DOI: 10.1007/s11517-024-03072-5] [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: 09/19/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different patterns. However, the extraction of characteristic information becomes increasingly difficult as the number of EEG types increases. To address this issue, a creative EEG classification technique is proposed by employing a principal component analysis network (PCANet) coupled with phase space reconstruction (PSR) and power spectrum density (PSD). We have introduced the PSR and PSD to prepare the inputs, where dynamic and frequency information are exposed from deep within PCANet. It is remarkable that a layered cascade strategy is designed to make a powerful deep learner according to the rule of one network vs one task (OVO). The proposed method has achieved greater effects than the individual models and shown superior performance in comparison with state-of-the-art algorithms, which present 98.0% of sensitivity, 99.90% of specificity, and 99.07% of accuracy. Our ensemble PCANet model works in an assembly line-like manner, obviating the need for hand-craft features. Results demonstrate that the proposed scheme can greatly enhances the accuracy and robustness of seizure detection from EEG signals.
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Affiliation(s)
- Chang Liu
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Wanzhong Chen
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China
| | - Mingyang Li
- College of Communication Engineering, Jilin University, Ren Min Street 5988, Changchun, China.
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Yedurkar DP, Metkar SP, Stephan T. Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal. Cogn Neurodyn 2024; 18:301-315. [PMID: 38699601 PMCID: PMC11061070 DOI: 10.1007/s11571-021-09773-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/10/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
Currently, with the bloom in artificial intelligence (AI) algorithms, various human-centered smart systems can be utilized, especially in cognitive computing, for the detection of various chronic brain diseases such as epileptic seizure. The primary goal of this research article is to propose a novel human-centered cognitive computing (HCCC) method for segment-wise seizure classification by employing multiresolution extracted data with directed transfer function (DTF) features, termed as the multiresolution directed transfer function (MDTF) approach. Initially, the multiresolution information of the epileptic seizure signal is extracted using a multiresolution adaptive filtering (MRAF) method. These seizure details are passed to the DTF where the information flow of high frequency bands is computed. Thereafter, different measures of complexity such as approximate entropy (AEN) and sample entropy (SAEN) are computed from the extracted high frequency bands. Lastly, a k-nearest neighbor (k-NN) and support vector machine (SVM) are used for classifying the EEG signal into non-seizure and seizure data depending on the multiresolution based information flow characteristics. The MDTF approach is tested on a standard dataset and validated using a dataset from a local hospital. The proposed technique has obtained an average sensitivity of 98.31%, specificity of 96.13% and accuracy of 98.89% using SVM classifier. The average detection rate of the MDTF approach is 97.72% which is greater than the existing approaches. The proposed MDTF method will help neuro-specialists to locate seizure information drift which occurs within the consecutive segments and between two channels. The main advantage of the MDTF approach is its capability to locate the seizure activity contained by the EEG signal with accuracy. This will assist the neurologists with the precise localization of the epileptic seizure automatically and hence will reduce the burden of time-consuming epileptic seizure analysis.
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Affiliation(s)
- Dhanalekshmi P. Yedurkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Shilpa P. Metkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka 560054 India
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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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Wijayanto I, Humairani A, Hadiyoso S, Rizal A, Prasanna DL, Tripathi SL. Epileptic seizure detection on a compressed EEG signal using energy measurement. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Pre-processed and combined EEG data for epileptic seizure classification using deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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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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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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Dhull MS, Kumar A, Wyłomańska A. The expectation-maximization algorithm for autoregressive models with normal inverse Gaussian innovations. COMMUN STAT-SIMUL C 2023. [DOI: 10.1080/03610918.2023.2186334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Affiliation(s)
- Monika S. Dhull
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, India
| | - Arun Kumar
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, India
| | - Agnieszka Wyłomańska
- Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wroclaw, Poland
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10
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A Review on the Applications of Time-Frequency Methods in ECG Analysis. JOURNAL OF HEALTHCARE ENGINEERING 2023. [DOI: 10.1155/2023/3145483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.
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11
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GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104274] [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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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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Wu X, Zhang T, Zhang L, Qiao L. Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network. Front Neurosci 2022; 16:982541. [PMID: 36225738 PMCID: PMC9548615 DOI: 10.3389/fnins.2022.982541] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/24/2022] [Indexed: 12/01/2022] Open
Abstract
As one of the most common neurological disorders, epilepsy causes great physical and psychological damage to the patients. The long-term recurrent and unprovoked seizures make the prediction necessary. In this paper, a novel approach for epileptic seizure prediction based on successive variational mode decomposition (SVMD) and transformers is proposed. SVMD is extended to multidimensional form for time-frequency analysis of multi-channel signals. It could adaptively extract common band-limited intrinsic modes among all channels on different time scales by solving a variational optimization problem. In the proposed seizure prediction method, data are first decomposed into multiple modes on different time scales by multivariate SVMD, and then, irrelevant modes are removed for preprocessing. Finally, power spectrum of denoised data is input to a pre-trained bidirectional encoder representations from transformers (BERTs) for prediction. The BERT could identify the mode information related to epileptic seizures in time-frequency domain. It shows fair prediction performance on an intracranial EEG dataset with the average sensitivity of 0.86 and FPR of 0.18/h.
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Affiliation(s)
- Xiao Wu
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Tinglin Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Limei Zhang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
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Interpretable seizure detection with signal temporal logic neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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15
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Dash DP, Kolekar MH, Chakraborty C, Khosravi MR. Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3552512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizure.
Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detetion is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summerizing it will give a new prospective to the reader.
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Feng Y, Zheng R, Cui X, Wang T, Jiang T, Gao F, Cao J. 3D residual-attention-deep-network-based childhood epilepsy syndrome classification. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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17
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Ibrahim FE, Emara HM, El-Shafai W, Elwekeil M, Rihan M, Eldokany IM, Taha TE, El-Fishawy AS, El-Rabaie ESM, Abdellatef E, Abd El-Samie FE. Deep-learning-based seizure detection and prediction from electroencephalography signals. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3573. [PMID: 35077027 DOI: 10.1002/cnm.3573] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Abstract
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.
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Affiliation(s)
- Fatma E Ibrahim
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Heba M Emara
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Walid El-Shafai
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, Saudi Arabia
| | - Mohamed Elwekeil
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Mohamed Rihan
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio, Cassino, 03043, Italy
| | - Ibrahim M Eldokany
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Taha E Taha
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Adel S El-Fishawy
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - El-Sayed M El-Rabaie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
| | - Essam Abdellatef
- Delta Higher Institute for Engineering and Technology (DHIET), Mansoura, Egypt
| | - Fathi E Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
- Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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Harishvijey A, Benadict Raja J. Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian Filter and Fuzzy RBFELM classifier. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103450] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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19
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Yan X, Yang D, Lin Z, Vucetic B. Significant Low-dimensional Spectral-temporal Features for Seizure Detection. IEEE Trans Neural Syst Rehabil Eng 2022; 30:668-677. [PMID: 35245199 DOI: 10.1109/tnsre.2022.3156931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Absence seizure as a generalized onset seizure, simultaneously spreading seizure to both sides of the brain, involves around ten-second sudden lapses of consciousness. It common occurs in children than adults, which affects living quality even threats lives. Absence seizure can be confused with inattentive attention-deficit hyperactivity disorder since both have similar symptoms, such as inattention and daze. Therefore, it is necessary to detect absence seizure onset. However, seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.
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Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Rajendra Acharya U, Gorriz JM. Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103417] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Tuncer E, Doğru Bolat E. Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (Bi-LSTM) network architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103462] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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22
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An intelligent epilepsy seizure detection system using adaptive mode decomposition of EEG signals. Phys Eng Sci Med 2022; 45:261-272. [PMID: 35167045 DOI: 10.1007/s13246-022-01111-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 02/07/2022] [Indexed: 10/19/2022]
Abstract
Epilepsy is a chronic neurological disorder that involves abnormal electrical signal patterns of the brain called seizures. The brain's electrical signals can be recorded using an electroencephalogram (EEG). EEG recordings can be used to monitor complex and non-stationary signals produced by the brain for detecting epilepsy seizures. Machine learning (ML) methods have been successfully applied in different domains to accurately classify the patterns based upon dataset features. However, ML methods are unable to analyze the raw EEG signals. Appropriate features must be extracted from EEG recordings for detecting epilepsy seizures using signal processing methods. This work proposes an intelligent system by integrating variational mode decomposition (VMD) and Hilbert transform (HT) method for extracting useful features from EEG signals and stacked neural network (NN) method for detecting epilepsy seizures. VMD method decomposers EEG signals into intrinsic mode functions, followed by the HT method for extracting features from EEG signals. The stacked-NN approach is applied for detecting epilepsy seizures using extracted features. The performance of the proposed system is validated using benchmark datasets for epilepsy seizure detection provided by Bonn University and, Neurology and Sleep Centre, New Delhi (NSC-ND). The performance of the proposed system is compared with other ML methods and state of the art approaches in the field. The reported results demonstrate that the proposed system can detect up to 100% accurate epilepsy seizures using NSC-ND data set and up to 99% accurate epilepsy seizures using Bonn university dataset. The comparative results also demonstrate the better performance of the proposed system over other ML methods and existing approaches for detecting epilepsy seizures. The remarkable performance of the proposed system can help neurological experts to detect epilepsy seizures accurately using EEG signals and can be embedded into the real-time diagnosis of the disease.
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23
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Zhang S, Liu G, Xiao R, Cui W, Cai J, Hu X, Sun Y, Qiu J, Qi Y. A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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24
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Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C. Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals. IEEE J Biomed Health Inform 2022; 26:527-538. [PMID: 34314363 DOI: 10.1109/jsen.2021.3057076] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms (EEGs) that can anticipate an epileptic seizure by differentiating between the pre-ictal and interictal stages of the subject's brain. Despite having the appearance of a typical anomaly detection task, this problem is complicated by subject-specific characteristics in EEG data. Therefore, studies that investigate seizure prediction widely employ subject-specific models. However, this approach is not suitable in situations where a target subject has limited (or no) data for training. Subject-independent models can address this issue by learning to predict seizures from multiple subjects, and therefore are of greater value in practice. In this study, we propose a subject-independent seizure predictor using Geometric Deep Learning (GDL). In the first stage of our GDL-based method we use graphs derived from physical connections in the EEG grid. We subsequently seek to synthesize subject-specific graphs using deep learning. The models proposed in both stages achieve state-of-the-art performance using a one-hour early seizure prediction window on two benchmark datasets (CHB-MIT-EEG: 95.38% with 23 subjects and Siena-EEG: 96.05% with 15 subjects). To the best of our knowledge, this is the first study that proposes synthesizing subject-specific graphs for seizure prediction. Furthermore, through model interpretation we outline how this method can potentially contribute towards Scalp EEG-based seizure localization.
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25
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Luo T, Wang J, Zhou Y, Zhou S, Hu C, Yao P, Zhang Y, Wang Y. EMD-WOG-2DCNN based EEG signal processing for Rolandic seizure classification. Comput Methods Biomech Biomed Engin 2022; 25:1565-1575. [PMID: 35044293 DOI: 10.1080/10255842.2021.2023809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Objective Approximately 65 million people have epilepsy around the world. Recognition of epilepsy types is the basis to determine the treatment method and predict the prognosis in epilepsy patients. Childhood benign epilepsy with centrotemporal spikes (BECTS) or benign Rolandic epilepsy is the most common focal epilepsy in children, accounting for 15-20% of childhood epilepsies. These EEG patterns of individuals usually predict good treatment responses and prognosis. Until now, the interpretation of EEG still depends entirely on experienced neurologists, which may be a lengthy and tedious task. Method In this article, we proposed a novel machine learning model that efficiently distinguished Rolandic seizures from normal EEG signals. The proposed machine learning model processes the identification procedure in the following order (1) creating preliminary EEG features using signal empirical mode decomposition, (2) applying weighted overlook graph (WOG) to represent the decomposed EMD of IMF, and (3) classifying the results through a two Dimensional Convolutional Neural Network (2DCNN). The performance of our classification model is compared with other representative machine learning models. Results The model offered in this article gains an accuracy performance exceeding 97.6% in the Rolandic dataset, which is higher than other classification models. The effect of the model on the Bonn public dataset is also comparable to existing methods and even performs better in some subsets. Conclusion The purpose of this study is to introduce the most common childhood benign epilepsy type and propose a model that meets the real clinical needs to distinguish this Rolandic EEG pattern from normal signals accurately. Significance Future research will optimize the model to categorize other types of epilepsies beyond BECTS and finally implement them in the hospital system.
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Affiliation(s)
- Tian Luo
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Jialin Wang
- The Key Laboratory of ASIC and Systems, The Institute of Brain-Inspired Circuits and Systems, Fudan University, Shanghai, China
| | - Yuanfeng Zhou
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Shuizhen Zhou
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Chunhui Hu
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Peili Yao
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Yanjiong Zhang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
| | - Yi Wang
- Department of Neurology, Children's Hospital of Fudan University, Shanghai, China
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26
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Song Z, Deng B, Wang J, Yi G, Yue W. Epileptic seizure detection using brain-rhythmic recurrence biomarkers and ONASNet-based transfer learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:979-989. [DOI: 10.1109/tnsre.2022.3165060] [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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27
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Abdulhussien AS, AbdulSaddaa AT, Iqbal K. Automatic seizure detection with different time delays using SDFT and time-domain feature extraction. J Biomed Res 2022; 36:48-57. [PMID: 35403610 PMCID: PMC8894282 DOI: 10.7555/jbr.36.20210124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Affiliation(s)
- Amal S. Abdulhussien
- Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, Al-Najaf 54001, Iraq
- Amal Salman Abdulhussien, Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, abylon-najaf street, Al-Najaf 540001, Iraq. Tel: +964-771-674-2333. E-mail:
| | - Ahmad T. AbdulSaddaa
- Communication Technical Engineering College, Al-Furat Al-Awsat Technical University, Al-Najaf 54001, Iraq
| | - Kamran Iqbal
- Department of System Engineering, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
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28
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Xue W, Zhou T, Cai J. Horizontal progressive and longitudinal leapfrogging fuzzy classification with feature activity adjustment. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108511] [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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29
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Kaleem M, Guergachi A, Krishnan S. Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach. Front Digit Health 2021; 3:738996. [PMID: 34966902 PMCID: PMC8710482 DOI: 10.3389/fdgth.2021.738996] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/18/2021] [Indexed: 11/23/2022] Open
Abstract
Analysis of long-term multichannel EEG signals for automatic seizure detection is an active area of research that has seen application of methods from different domains of signal processing and machine learning. The majority of approaches developed in this context consist of extraction of hand-crafted features that are used to train a classifier for eventual seizure detection. Approaches that are data-driven, do not use hand-crafted features, and use small amounts of patients' historical EEG data for classifier training are few in number. The approach presented in this paper falls in the latter category, and is based on a signal-derived empirical dictionary approach, which utilizes empirical mode decomposition (EMD) and discrete wavelet transform (DWT) based dictionaries learned using a framework inspired by traditional methods of dictionary learning. Three features associated with traditional dictionary learning approaches, namely projection coefficients, coefficient vector and reconstruction error, are extracted from both EMD and DWT based dictionaries for automated seizure detection. This is the first time these features have been applied for automatic seizure detection using an empirical dictionary approach. Small amounts of patients' historical multi-channel EEG data are used for classifier training, and multiple classifiers are used for seizure detection using newer data. In addition, the seizure detection results are validated using 5-fold cross-validation to rule out any bias in the results. The CHB-MIT benchmark database containing long-term EEG recordings of pediatric patients is used for validation of the approach, and seizure detection performance comparable to the state-of-the-art is obtained. Seizure detection is performed using five classifiers, thereby allowing a comparison of the dictionary approaches, features extracted, and classifiers used. The best seizure detection performance is obtained using EMD based dictionary and reconstruction error feature and support vector machine classifier, with accuracy, sensitivity and specificity values of 88.2, 90.3, and 88.1%, respectively. Comparison is also made with other recent studies using the same database. The methodology presented in this paper is shown to be computationally efficient and robust for patient-specific automatic seizure detection. A data-driven methodology utilizing a small amount of patients' historical data is hence demonstrated as a practical solution for automatic seizure detection.
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Affiliation(s)
- Muhammad Kaleem
- Department of Electrical Engineering, University of Management and Technology, Lahore, Pakistan
| | - Aziz Guergachi
- Department of Information Technology Management, Ted Rogers School of Management, Ryerson University, Toronto, ON, Canada
| | - Sridhar Krishnan
- Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON, Canada
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30
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Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy. Neurosci Lett 2021; 765:136269. [PMID: 34582974 DOI: 10.1016/j.neulet.2021.136269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/19/2021] [Accepted: 09/23/2021] [Indexed: 11/20/2022]
Abstract
Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neuro-pathological condition called encephalopathy. Feature combination is accomplished with the help of feature selection using Gini impurity score that improves discriminative power and keeps redundancy minimal. Further, three widely used non-parametric classifiers which are known to be effective with wavelet features on EEG signals - Support Vector Machine, Random Forest, Multilayer Perceptron - are employed for disease classification. The models created by the combination of aforementioned stages are analysed and evaluated with performance scores, leading to an optimal model for automated diagnostic applications.
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31
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Boonyakitanont P, Lek-Uthai A, Songsiri J. ScoreNet: A Neural Network-Based Post-Processing Model for Identifying Epileptic Seizure Onset and Offset in EEGs. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2474-2483. [PMID: 34797766 DOI: 10.1109/tnsre.2021.3129467] [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: 11/06/2022]
Abstract
We design an algorithm to automatically detect epileptic seizure onsets and offsets from scalp electroencephalograms (EEGs). The proposed scheme consists of two sequential steps: detecting seizure episodes from long EEG recordings, and determining seizure onsets and offsets of the detected episodes. We introduce a neural network-based model called ScoreNet to carry out the second step by better predicting the seizure probability of pre-detected seizure epochs to determine seizure onsets and offsets. A cost function called log-dice loss with a similar meaning to the F1 score is proposed to handle the natural data imbalance inherent in EEG signals signifying seizure events. ScoreNet is then verified on the CHB-MIT Scalp EEG database in combination with several classifiers including random forest, convolutional neural network (CNN), and logistic regression. As a result, ScoreNet improves seizure detection performance over lone epoch-based seizure classification methods; F1 scores increase significantly from 16-37% to 53-70%, and false positive rates per hour decrease from 0.53-5.24 to 0.05-0.61. This method provides clinically acceptable latencies of detecting seizure onset and offset of less than 10 seconds. In addition, an effective latency index is proposed as a metric for detection latency whose scoring considers undetected events to provide better insight into onset and offset detection than conventional time-based metrics.
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32
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ElSayed NE, Tolba AS, Rashad MZ, Belal T, Sarhan S. Multimodal analysis of electroencephalographic and electrooculographic signals. Comput Biol Med 2021; 137:104809. [PMID: 34517160 DOI: 10.1016/j.compbiomed.2021.104809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/24/2021] [Accepted: 08/24/2021] [Indexed: 11/18/2022]
Abstract
Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R2 = 1.00 outperforming the other two approaches with R2 = 0.80 for DT and R2 = 0.76 for SVM.
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Affiliation(s)
- Nesma E ElSayed
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt; Delta Higher Institute for Computers, 35511, Egypt
| | - A S Tolba
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt
| | - M Z Rashad
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt
| | - Tamer Belal
- Department of Neurology, Faculty of Medicine, Mansoura University, 35511, Egypt
| | - Shahenda Sarhan
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, 35511, Egypt.
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33
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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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34
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Chakrabarti S, Swetapadma A, Pattnaik PK. A channel independent generalized seizure detection method for pediatric epileptic seizures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106335. [PMID: 34390934 DOI: 10.1016/j.cmpb.2021.106335] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 07/27/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework. METHOD In this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture. RESULTS The automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures. CONCLUSION Assessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.
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Affiliation(s)
- Satarupa Chakrabarti
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aleena Swetapadma
- School of Computer Engineering, KIIT University, Bhubaneswar, Odisha 751024, India.
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35
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He H, Liu X, Hao Y. A progressive deep wavelet cascade classification model for epilepsy detection. Artif Intell Med 2021; 118:102117. [PMID: 34412840 DOI: 10.1016/j.artmed.2021.102117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 04/11/2021] [Accepted: 05/21/2021] [Indexed: 12/27/2022]
Abstract
Automatic epileptic seizure detection according to EEG recordings is helpful for neurologists to identify an epilepsy occurrence in the initial anti-epileptic treatment. To quickly and accurately detect epilepsy, we proposed a progressive deep wavelet cascade classification model (PDWC) based on the discrete wavelet transform (DWT) and Random Forest (RF). Different from current deep networks, the PDWC mimics the progressive object identification process of human beings with recognition cycles. In every cycle, enhanced wavelet energy features at a specific scale were extracted by DWT and input into a set of cascade RF classifiers to realize one recognition. The recognition accuracy of PDWC is gradually improved by the fusion of classification results produced by multiple recognition cycles. Moreover, the cascade structure of PDWC can be automatically determined by the classification accuracy increment between layers. To verify the performance of the PDWC, we respectively applied five traditional schemes and four deep learning schemes to four public datasets. The results show that the PDWC is not only superior than five traditional schemes, including KNN, Bayes, DT, SVM, and RF, but also better than deep learning methods, i.e. convolutional neural network (CNN), Long Short-Term Memory (LSTM), multi-Grained Cascade Forest (gcForest) and wavelet cascade model (WCM). The mean accuracy of PDWC for all subjects of all datasets reaches to 0.9914. With a flexible structure and less parameters, the PDWC is more suitable for the epilepsy detection of diverse EEG signals.
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Affiliation(s)
- Hong He
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Xinyue Liu
- College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
| | - Yong Hao
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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36
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Anuragi A, Sisodia DS, Pachori RB. Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals. Comput Biol Med 2021; 136:104708. [PMID: 34358996 DOI: 10.1016/j.compbiomed.2021.104708] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/25/2021] [Accepted: 07/25/2021] [Indexed: 11/19/2022]
Abstract
Epilepsy is a neurological disorder that has severely affected many people's lives across the world. Electroencephalogram (EEG) signals are used to characterize the brain's state and detect various disorders. The EEG signals are non-stationary and non-linear in nature. Therefore, it is challenging to accurately process and learn from the recorded EEG signals in order to detect disorders like epilepsy. This paper proposed an automated learning framework using the Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) method for detecting epileptic seizures from EEG signals. The scale-space boundary detection method was adopted to segment the Fourier-Bessel series expansion (FBSE) spectrum of multiple frame-size time-segmented EEG signals. Multiple frame-size time-segmented EEG signal's analysis was done using four different frame sizes: full, half, quarter, and half-quarter length of recorded EEG signals. Two different time-segmentation approaches were investigated on EEG signals: 1) segmenting signals based on multiple frame-size and 2) segmenting signals based on multiple frame-size with zero-padding the remaining signal. The FBSE-EWT method was applied to decompose the EEG signals into narrow sub-band signals. Features such as line-length (LL), log-energy-entropy (LEnt), and norm-entropy (NEnt) were computed from various frequency range sub-band signals. The relief-F feature ranking method was employed to select the most significant features; this reduces the computational burden of the models. The top-ranked accumulated features were used for classification using least square-support machine learning (LS-SVM), support vector machine (SVM), k-nearest neighbor (k-NN), and ensemble bagged tree classifiers. The proposed framework for epileptic seizure detection was evaluated on two publicly available benchmark EEG datasets: the Bonn EEG dataset and Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), well known as the CHB-MIT scalp EEG dataset. Training and testing of the models were performed using the 10-fold cross-validation technique. The FBSE-EWT based learning framework was compared with other state-of-the-art methods using both datasets. Experimental results showed that the proposed framework achieved 100 % classification accuracy on the Bonn EEG dataset, whereas 99.84 % classification accuracy on the CHB-MIT scalp EEG dataset.
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Affiliation(s)
- Arti Anuragi
- Department of Computer Science & Engineering, National Institute of Technology Raipur, G E Road, Raipur, Chhattisgarh, 492010, India.
| | - Dilip Singh Sisodia
- Department of Computer Science & Engineering, National Institute of Technology Raipur, G E Road, Raipur, Chhattisgarh, 492010, India.
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Indore, Madhya predesh, 453552, India.
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37
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Epileptic seizure detection using novel Multilayer LSTM Discriminant Network and dynamic mode Koopman decomposition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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38
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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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39
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Qaisar SM, Hussain SF. Effective epileptic seizure detection by using level-crossing EEG sampling sub-bands statistical features selection and machine learning for mobile healthcare. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106034. [PMID: 33744752 DOI: 10.1016/j.cmpb.2021.106034] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/25/2021] [Indexed: 06/12/2023]
Abstract
Mobile healthcare is an emerging approach which can be realized by using cloud-connected biomedical implants. In this context, a level-crossing sampling and adaptive-rate processing based innovative method is suggested for an effective and automated epileptic seizures diagnosis. The suggested solution can achieve a significant real-time compression in computational complexity and transmission activity reduction. The proposed method acquires the electroencephalogram (EEG) signal by using the level-crossing analog-to-digital converter (LCADC) and selects its active segments by using the activity selection algorithm (ASA). This effectively pilots the post adaptive-rate modules such as denoising, wavelet based sub-bands decomposition, and dimension reduction. The University of Bonn and Hauz Khas epilepsy-detection databases are used to evaluate the proposed approach. Experiments show that the proposed system achieves a 4.1-fold and 3.7-fold decline, respectively, for University of Bonn and Hauz Khas datasets, in the number of samples obtained as opposed to traditional counterparts. This results in a reduction of the computational complexity of the proposed adaptive-rate processing approach by more than 14-fold. It promises a noticeable reduction in transmitter power, the use of bandwidth, and cloud-based classifier computational load. The overall accuracy of the method is also quantified in terms of the epilepsy classification performance. The proposed system achieves100% classification accuracy for most of the studied cases.
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Affiliation(s)
- Saeed Mian Qaisar
- Electrical and Computer Engineering Department, Effat University, Jeddah, 22332, KSA; Communication & Signal Processing Lab, Energy & Technology Cenetr, Effat University, Jeddah, 22332, KSA.
| | - Syed Fawad Hussain
- Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan; Machine Learning and Data Science (MDS) lab, GIK Institute, Topi.
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40
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An efficient method for identification of epileptic seizures from EEG signals using Fourier analysis. Phys Eng Sci Med 2021; 44:443-456. [PMID: 33779946 DOI: 10.1007/s13246-021-00995-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/18/2021] [Indexed: 10/21/2022]
Abstract
Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells. Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy. In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method. The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using [Formula: see text] norms computed from Fourier intrinsic band functions (FIBFs). The proposed scheme comprises three main sections. In the first section, the EEG signal is decomposed into a finite number of FIBFs. In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal-Wallis test. In the last stage, the significant features are passed on to the support vector machine (SVM) classifier. By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 99.96% and 99.94% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively. It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm.
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41
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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: 15] [Impact Index Per Article: 5.0] [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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42
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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: 9] [Impact Index Per Article: 3.0] [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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43
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Epilepsy seizure detection using kurtosis based VMD’s parameters selection and bandwidth features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102255] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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44
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Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2020.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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45
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Tobore I, Kandwal A, Li J, Yan Y, Omisore OM, Enitan E, Sinan L, Yuhang L, Wang L, Nie Z. Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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46
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Supriya S, Siuly S, Wang H, Zhang Y. Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf Sci Syst 2020; 8:33. [PMID: 33088489 DOI: 10.1007/s13755-020-00129-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5-29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.
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Affiliation(s)
- Supriya Supriya
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
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47
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Carvalho VR, Moraes MF, Braga AP, Mendes EM. Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102073] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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48
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Identification of Epileptic EEG Signals Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10124089] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
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