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Wang Z, Liu F, Shi S, Xia S, Peng F, Wang L, Ai S, Xu Z. Automatic epileptic seizure detection based on persistent homology. Front Physiol 2023; 14:1227952. [PMID: 38192741 PMCID: PMC10773586 DOI: 10.3389/fphys.2023.1227952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024] Open
Abstract
Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris-Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22 s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.
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Affiliation(s)
- Ziyu Wang
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Feifei Liu
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Shuhua Shi
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Shengxiang Xia
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Fulai Peng
- Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
| | - Lin Wang
- The Fifth People’s Hospital of Jinan, Jinan, China
| | - Sen Ai
- School of Science, Shandong Jianzhu University, Jinan, China
| | - Zheng Xu
- School of Science, Shandong Jianzhu University, Jinan, China
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Prabhakar SK, Won DO. Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals. Front Artif Intell 2023; 6:1156269. [PMID: 37415937 PMCID: PMC10321130 DOI: 10.3389/frai.2023.1156269] [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: 02/01/2023] [Accepted: 05/25/2023] [Indexed: 07/08/2023] Open
Abstract
A comprehensive analysis of an automated system for epileptic seizure detection is explained in this work. When a seizure occurs, it is quite difficult to differentiate the non-stationary patterns from the discharges occurring in a rhythmic manner. The proposed approach deals with it efficiently by clustering it initially for the sake of feature extraction by using six different techniques categorized under two different methods, e.g., bio-inspired clustering and learning-based clustering. Learning-based clustering includes K-means clusters and Fuzzy C-means (FCM) clusters, while bio-inspired clusters include Cuckoo search clusters, Dragonfly clusters, Firefly clusters, and Modified Firefly clusters. Clustered values were then classified with 10 suitable classifiers, and after the performance comparison analysis of the EEG time series, the results proved that this methodology flow achieved a good performance index and a high classification accuracy. A comparatively higher classification accuracy of 99.48% was achieved when Cuckoo search clusters were utilized with linear support vector machines (SVM) for epilepsy detection. A high classification accuracy of 98.96% was obtained when K-means clusters were classified with a naive Bayesian classifier (NBC) and Linear SVM, and similar results were obtained when FCM clusters were classified with Decision Trees yielding the same values. The comparatively lowest classification accuracy, at 75.5%, was obtained when Dragonfly clusters were classified with the K-nearest neighbor (KNN) classifier, and the second lowest classification accuracy of 75.75% was obtained when Firefly clusters were classified with NBC.
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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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Prabhakar SK, Ju YG, Rajaguru H, Won DO. Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification. Front Comput Neurosci 2022; 16:1016516. [DOI: 10.3389/fncom.2022.1016516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
In comparison to other biomedical signals, electroencephalography (EEG) signals are quite complex in nature, so it requires a versatile model for feature extraction and classification. The structural information that prevails in the originally featured matrix is usually lost when dealing with standard feature extraction and conventional classification techniques. The main intention of this work is to propose a very novel and versatile approach for EEG signal modeling and classification. In this work, a sparse representation model along with the analysis of sparseness measures is done initially for the EEG signals and then a novel convergence of utilizing these sparse representation measures with Swarm Intelligence (SI) techniques based Hidden Markov Model (HMM) is utilized for the classification. The SI techniques utilized to compute the hidden states of the HMM are Particle Swarm Optimization (PSO), Differential Evolution (DE), Whale Optimization Algorithm (WOA), and Backtracking Search Algorithm (BSA), thereby making the HMM more pliable. Later, a deep learning methodology with the help of Convolutional Neural Network (CNN) was also developed with it and the results are compared to the standard pattern recognition classifiers. To validate the efficacy of the proposed methodology, a comprehensive experimental analysis is done over publicly available EEG datasets. The method is supported by strong statistical tests and theoretical analysis and results show that when sparse representation is implemented with deep learning, the highest classification accuracy of 98.94% is obtained and when sparse representation is implemented with SI-based HMM method, a high classification accuracy of 95.70% is obtained.
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Kumar A. A new fitness function in genetic programming for classification of imbalanced data. J EXP THEOR ARTIF IN 2022. [DOI: 10.1080/0952813x.2022.2120087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Arvind Kumar
- Location Intelligence R&D, Precisely Software and Data India Private Limited, Noida, India
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Personality Prediction with Hybrid Genetic Programming using Portable EEG Device. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4867630. [PMID: 35694595 PMCID: PMC9177303 DOI: 10.1155/2022/4867630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
Abstract
This work suggests a method to identify personality traits regarding the targeted film clips in real-time. Such film clips elicit feelings in people while capturing their brain impulses using the electroencephalogram (EEG) devices and examining personality traits. The Myers–Briggs Type Indicator (MBTI) paradigm for determining personality is employed in this study. The fast Fourier transform (FFT) approach is used for feature extraction, and we have used hybrid genetic programming (HGP) for EEG data classification. We used a single-channel NeuroSky MindWave 2 dry electrode unit to obtain the EEG data. In order to collect the data, thirty Hindi and English video clips were placed in a conventional database. Fifty people volunteered to participate in this study and willingly provided brain signals. Using this dataset, we have generated four two-class HGP classifiers (HGP1, HGP2, HGP3, and HGP4), one for each group of MBTI traits overall classification accuracy of the HGP classifier as 82.25% for 10-fold cross-validation partition.
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Prabhakar SK, Rajaguru H, Kim C, Won DO. A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification. Front Hum Neurosci 2022; 16:895761. [PMID: 35721347 PMCID: PMC9203681 DOI: 10.3389/fnhum.2022.895761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/02/2022] [Indexed: 12/02/2022] Open
Abstract
The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.
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Affiliation(s)
- Sunil Kumar Prabhakar
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
| | - Chulho Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Chuncheon, South Korea
| | - Dong-Ok Won
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
- *Correspondence: Dong-Ok Won,
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Nasrolahzadeh M, Rahnamayan S, Haddadnia J. Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2021.100225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Kaur T, Diwakar A, Kirandeep, Mirpuri P, Tripathi M, Chandra PS, Gandhi TK. Artificial Intelligence in Epilepsy. Neurol India 2021; 69:560-566. [PMID: 34169842 DOI: 10.4103/0028-3886.317233] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background The study of seizure patterns in electroencephalography (EEG) requires several years of intensive training. In addition, inadequate training and human error may lead to misinterpretation and incorrect diagnosis. Artificial intelligence (AI)-based automated seizure detection systems hold an exciting potential to create paradigms for proper diagnosis and interpretation. AI holds the promise to transform healthcare into a system where machines and humans can work together to provide an accurate, timely diagnosis, and treatment to the patients. Objective This article presents a brief overview of research on the use of AI systems for pattern recognition in EEG for clinical diagnosis. Material and Methods The article begins with the need for understanding nonstationary signals such as EEG and simplifying their complexity for accurate pattern recognition in medical diagnosis. It also explains the core concepts of AI, machine learning (ML), and deep learning (DL) methods. Results and Conclusions In this present context of epilepsy diagnosis, AI may work in two ways; first by creating visual representations (e.g., color-coded paradigms), which allow persons with limited training to make a diagnosis. The second is by directly explaining a complete automated analysis, which of course requires more complex paradigms than the previous one. We also clarify that AI is not about replacing doctors and strongly emphasize the need for domain knowledge in building robust AI models that can work in real-time scenarios rendering good detection accuracy in a minimum amount of time.
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Affiliation(s)
- Taranjit Kaur
- Department of Electrical, Engineering, IIT Delhi, New Delhi, India
| | | | - Kirandeep
- Department of Neuroscience, AIIMS, New Delhi, India
| | | | | | | | - Tapan K Gandhi
- Department of Electrical, Engineering, IIT Delhi, New Delhi, India
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Zarei A, Asl BM. Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals. Comput Biol Med 2021; 131:104250. [PMID: 33578071 DOI: 10.1016/j.compbiomed.2021.104250] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/28/2021] [Accepted: 01/28/2021] [Indexed: 10/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is a prevalent disorder that affects the central nervous system, causing seizures. In the current study, a novel algorithm is developed using electroencephalographic (EEG) signals for automatic seizure detection from the continuous EEG monitoring data. METHODS In the proposed methods, the discrete wavelet transform (DWT) and orthogonal matching pursuit (OMP) techniques are used to extract different coefficients from the EEG signals. Then, some non-linear features, such as fuzzy/approximate/sample/alphabet and correct conditional entropy, along with some statistical features are calculated using the DWT and OMP coefficients. Three widely-used EEG datasets were utilized to assess the performance of the proposed techniques. RESULTS The proposed OMP-based technique along with the support vector machine classifier yielded an average specificity of 96.58%, an average accuracy of 97%, and an average sensitivity of 97.08% for different types of classification tasks. Moreover, the proposed DWT-based technique provided an average sensitivity of 99.39%, an average accuracy of 99.63%, and an average specificity of 99.72%. CONCLUSIONS The experimental findings indicated that the proposed algorithms outperformed other existing techniques. Therefore, these algorithms can be implemented in relevant hardware to help neurologists with seizure detection.
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Affiliation(s)
- Asghar Zarei
- Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran
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Li M, Sun X, Chen W. Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals. Med Biol Eng Comput 2020; 58:3075-3088. [DOI: 10.1007/s11517-020-02279-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 10/20/2020] [Indexed: 11/30/2022]
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12
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Acharya D, Goel S, Asthana R, Bhardwaj A. A novel fitness function in genetic programming to handle unbalanced emotion recognition data. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.03.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Sharma R, Pachori RB, Sircar P. Seizures classification based on higher order statistics and deep neural network. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101921] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Li Y, Yu Z, Chen Y, Yang C, Li Y, Allen Li X, Li B. Automatic Seizure Detection using Fully Convolutional Nested LSTM. Int J Neural Syst 2020; 30:2050019. [DOI: 10.1142/s0129065720500197] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44–100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB–MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66 per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.
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Affiliation(s)
- Yang Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
| | - Zuyi Yu
- School of Information Science and Engineering, Shandong University, Jinan, Shandong 250100, P. R. China
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Chunfeng Yang
- Laboratory of Image Science and Technology, Southeast University, Nanjing, Jiangsu 210096, P. R. China
| | - Yue Li
- School of Clinical Medicine, Dali University, Dali, Yunnan 671000, P. R. China
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Baosheng Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, P. R. China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P. R. China
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Automatic focal and non-focal EEG detection using entropy-based features from flexible analytic wavelet transform. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101761] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Ramanna S, Tirunagari S, Windridge D. Epileptic seizure detection using constrained singular spectrum analysis and 1D-local binary patterns. HEALTH AND TECHNOLOGY 2020. [DOI: 10.1007/s12553-019-00395-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Mamli S, Kalbkhani H. Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Yuan Q, Zhou W, Xu F, Leng Y, Wei D. Epileptic EEG Identification via LBP Operators on Wavelet Coefficients. Int J Neural Syst 2018; 28:1850010. [DOI: 10.1142/s0129065718500107] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time–frequency decomposition of EEG signals. After that, the “uniform” LBP operator is carried out on the wavelet-based time–frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the “uniform” LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.
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Affiliation(s)
- Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250101, P. R. China
| | - Fangzhou Xu
- School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, P. R. China
| | - Yan Leng
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, P. R. China
| | - Dongmei Wei
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, P. R. China
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An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.01.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Ong P, Zainuddin Z, Lai KH. A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals. Pattern Anal Appl 2017. [DOI: 10.1007/s10044-017-0642-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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22
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Li M, Chen W, Zhang T. Application of MODWT and log-normal distribution model for automatic epilepsy identification. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2017.08.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Roshan Zamir Z. Detection of epileptic seizure in EEG signals using linear least squares preprocessing. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 133:95-109. [PMID: 27393803 DOI: 10.1016/j.cmpb.2016.05.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 05/09/2016] [Accepted: 05/14/2016] [Indexed: 06/06/2023]
Abstract
An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure.
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Affiliation(s)
- Z Roshan Zamir
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, PO Box 218, Hawthorn, Victoria, Australia.
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van Andel J, Thijs RD, de Weerd A, Arends J, Leijten F. Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care? Epilepsy Behav 2016; 57:82-89. [PMID: 26926071 DOI: 10.1016/j.yebeh.2016.01.003] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 12/31/2015] [Accepted: 01/02/2016] [Indexed: 12/31/2022]
Abstract
OBJECTIVE This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care. METHODS PubMed and EMBASE were searched up to November 2014, using variations and synonyms of search terms "seizure prediction" OR "seizure detection" OR "seizures" AND "alarm". RESULTS Seventeen studies evaluated performance of devices and algorithms to detect seizures in a clinical setting. Algorithms detecting generalized tonic-clonic seizures (GTCSs) had varying sensitivities (11% to 100%) and false alarm rates (0.2-4/24 h). For other seizure types, detection rates were low, or devices produced many false alarms. Five studies externally validated the performance of four different devices for the detection of GTCSs. Two devices were promising in both children and adults: a mattress-based nocturnal seizure detector (sensitivity: 84.6% and 100%; false alarm rate: not reported) and a wrist-based detector (sensitivity: 89.7%; false alarm rate: 0.2/24 h). SIGNIFICANCE Detection of seizure types other than GTCSs is currently unreliable. Two detection devices for GTCSs provided promising results when externally validated in a clinical setting. However, these devices need to be evaluated in the home setting in order to establish their true value. Automatic seizure detection may help prevent sudden unexpected death in epilepsy or status epilepticus, provided the alarm is followed by an effective intervention. Accurate seizure detection may improve the quality of life (QoL) of subjects and caregivers by decreasing burden of seizure monitoring and may facilitate diagnostic monitoring in the home setting. Possible risks are occurrence of alarm fatigue and invasion of privacy. Moreover, an unexpectedly high seizure frequency might be detected for which there are no treatment options. We propose that future studies monitor benefits and disadvantages of seizure detection systems with particular emphasis on QoL, comfort, and privacy of subjects and impact of false alarms.
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Affiliation(s)
- Judith van Andel
- University Medical Centre Utrecht, Department of Clinical Neurophysiology, Utrecht, The Netherlands.
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland SEIN, Department of Clinical Neurophysiology, Heemstede, The Netherlands; Leiden University Medical Centre, Department of Neurology, Leiden, The Netherlands
| | - Al de Weerd
- Stichting Epilepsie Instellingen Nederland SEIN, Department of Clinical Neurophysiology, Zwolle, The Netherlands
| | - Johan Arends
- Academic Centre for Epileptology Kempenhaeghe, Department of Clinical Neurophysiology, Heeze, The Netherlands
| | - Frans Leijten
- University Medical Centre Utrecht, Department of Clinical Neurophysiology, Utrecht, The Netherlands
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