1
|
Sathiya E, Rao TD, Kumar TS. Gabor filter-based statistical features for ADHD detection. Front Hum Neurosci 2024; 18:1369862. [PMID: 38660014 PMCID: PMC11039779 DOI: 10.3389/fnhum.2024.1369862] [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: 01/13/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.
Collapse
Affiliation(s)
- E. Sathiya
- Division of Mathematics, Vellore Institute of Technology, Chennai, India
| | - T. D. Rao
- Division of Mathematics, Vellore Institute of Technology, Chennai, India
| | - T. Sunil Kumar
- Department of Electrical Engineering, Mathematics and Science, University of Gävle, Gavle, Sweden
| |
Collapse
|
2
|
Zhong X, Liu G, Dong X, Li C, Li H, Cui H, Zhou W. Automatic Seizure Detection Based on Stockwell Transform and Transformer. SENSORS (BASEL, SWITZERLAND) 2023; 24:77. [PMID: 38202939 PMCID: PMC10781173 DOI: 10.3390/s24010077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024]
Abstract
Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.
Collapse
Affiliation(s)
- Xiangwen Zhong
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Guoyang Liu
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Xingchen Dong
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Chuanyu Li
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Haotian Li
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Haozhou Cui
- School of Integrated Circuits, Shandong University, Jinan 260100, China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 260100, China
- Shenzhen Institute, Shandong University, Shenzhen 518057, China
| |
Collapse
|
3
|
Khlif MS, Mesbah M, Colditz PB, Boashash B. Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107014. [PMID: 35849896 DOI: 10.1016/j.cmpb.2022.107014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG. METHODS Neonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics. RESULTS Compared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]). CONCLUSIONS The high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.
Collapse
Affiliation(s)
- Mohamed Salah Khlif
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, 30 Royal Parade, Parkville, VIC 3052, Australia; University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia
| | - Mostefa Mesbah
- Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, PO Box 33 PC 123, Al-Khoud, Muscat, Oman; University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia.
| | - Paul B Colditz
- University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia
| | - Boualem Boashash
- University of Queensland Centre for Clinical Research, The University of Queensland, Building 71/918, Royal Brisbane & Women's Hospital Campus, Herston, QLD 4029, Australia
| |
Collapse
|
4
|
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]
|
5
|
Guo Y, Jiang X, Tao L, Meng L, Dai C, Long X, Wan F, Zhang Y, van Dijk J, Aarts RM, Chen W, Chen C. Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble. IEEE Trans Neural Syst Rehabil Eng 2022; 30:915-924. [PMID: 35353703 DOI: 10.1109/tnsre.2022.3163503] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.
Collapse
|
6
|
Zhou J, Liu L, Leng Y, Yang Y, Gao B, Jiang Z, Nie W, Yuan Q. Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic. Int J Neural Syst 2022; 32:2250017. [DOI: 10.1142/s0129065722500174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.
Collapse
Affiliation(s)
- Jiazheng Zhou
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Li Liu
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, 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 250358, P. R. China
| | - Yuying Yang
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Bin Gao
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| | - Zonghong Jiang
- College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China
| | - Weiwei Nie
- The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong, Normal University, Jinan 250358, P. R. China
| |
Collapse
|
7
|
Zhang Y, Yao S, Yang R, Liu X, Qiu W, Han L, Zhou W, Shang W. Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network. IEEE Trans Neural Syst Rehabil Eng 2022; 30:135-145. [PMID: 35030083 DOI: 10.1109/tnsre.2022.3143540] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.
Collapse
|
8
|
Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.11.002] [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]
|
9
|
Gu H, Chou CA. Optimizing non-uniform multivariate embedding for multiscale entropy analysis of complex systems. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
10
|
Liu G, Tian L, Zhou W. Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory. Int J Neural Syst 2021; 32:2150051. [PMID: 34781854 DOI: 10.1142/s0129065721500519] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.
Collapse
Affiliation(s)
- Guoyang Liu
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Lan Tian
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China.,Shenzhen Institute of Shandong University, Shenzhen 518057, P. R. China
| |
Collapse
|
11
|
Gu H, Chou CA. Detecting Epileptic Seizures via Non-Uniform Multivariate Embedding of EEG Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1690-1693. [PMID: 34891611 DOI: 10.1109/embc46164.2021.9630130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Efficient real-time detection of epileptic seizures remains a challenging task in clinical practice. In this study, we introduce a new thresholding method to monitor brain activities via a non-uniform multivariate (NUM) embedding of multi-channel electroencephalogram (EEG) signals. Specifically, we present a NUM embedding optimization problem to identify the best embedding parameters. We originate one feature, named non-uniform multivariate multiscale entropy (NUMME), which is extracted from the NUM embedded EEG data. Finally, the extracted feature, compared to an individualized threshold, is used for monitoring and detecting seizure onsets. Experimental results on the real CHB-MIT Scalp EEG database show that our approach achieves a comparable performance to the state-of-art methods. Moreover, it is important to note that we accomplish this without using any sophisticated machine learning algorithms.
Collapse
|
12
|
Prasanna J, Subathra MSP, Mohammed MA, Damaševičius R, Sairamya NJ, George ST. Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database-A Survey. J Pers Med 2021; 11:1028. [PMID: 34683169 PMCID: PMC8537151 DOI: 10.3390/jpm11101028] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/10/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022] Open
Abstract
Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
Collapse
Affiliation(s)
- J. Prasanna
- Department of Electronics and Instrumentation Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; (J.P.); (N.J.S.)
| | - M. S. P. Subathra
- Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India;
| | - Mazin Abed Mohammed
- Information Systems Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31000, Anbar, Iraq;
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
| | - Nanjappan Jothiraj Sairamya
- Department of Electronics and Instrumentation Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; (J.P.); (N.J.S.)
| | - S. Thomas George
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
| |
Collapse
|
13
|
Automatic detection of epileptic seizure events using the time-frequency features and machine learning. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
|
14
|
Sahani M, Rout SK, Dash PK. Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:595-605. [PMID: 34156948 DOI: 10.1109/tbcas.2021.3090995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN) are combined to recognize the epileptic seizure using both the multichannel scalp and single-channel electroencephalogram (EEG) signals. The novel RDCSAE structure is designed to extract the most discriminative unsupervised features from EEG signals and fed into the proposed supervised IKRVFLN classifier to train efficiently by reducing the mean-square error cost function for recognizing the epileptic seizure activity with promising accuracy. The proposed RDCSAE-IKRVFLN algorithm is tested over the benchmark Boston Children's Hospital multichannel scalp EEG (sEEG) and Boon University, Germany single-channel EEG databases. The less computational complexity, higher learning speed, better model generalization, accurate epileptic seizure recognition, remarkable classification accuracy, negligible false positive rate per hour (FPR/h) and short event recognition time are the main advantages of the proposed RDCSAE-IKRVFLN method over reduced deep convolutional neural network (RDCNN), RDCSAE and RDCSAE-KRVFLN methods. The proposed RDCSAE-IKRVFLN method is implemented in a high-speed reconfigurable field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for automatic epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the stable and reliable performances of epilepsy detection and recognition.
Collapse
|
15
|
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.
Collapse
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
| | | |
Collapse
|
16
|
|
17
|
Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images. REMOTE SENSING 2020. [DOI: 10.3390/rs12244135] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.
Collapse
|
18
|
Scalp EEG classification using deep Bi-LSTM network for seizure detection. Comput Biol Med 2020; 124:103919. [DOI: 10.1016/j.compbiomed.2020.103919] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 06/29/2020] [Accepted: 07/14/2020] [Indexed: 11/15/2022]
|
19
|
Bognár G, Fridli S. ECG heartbeat classification by means of variable rational projection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
20
|
Molina E, Sarmiento Torres CE, Salazar-Cabrera R, López DM, Vargas-Cañas R. Intelligent Telehealth System To Support Epilepsy Diagnosis. J Multidiscip Healthc 2020; 13:433-445. [PMID: 32523350 PMCID: PMC7237117 DOI: 10.2147/jmdh.s247878] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/24/2020] [Indexed: 01/02/2023] Open
Abstract
Background Availability and opportunity of epilepsy diagnostic services is a significant challenge, especially in developing countries with a low number of neurologists. The most commonly used test to diagnose epilepsy is electroencephalogram (EEG). A typical EEG recording lasts for 20 to 30 minutes; however, a specialist requires much more time to read it. Furthermore, no evidence was found in the literature on open-source systems for the cost-effective management of patient information using electronic health records (EHR) that adequately integrate EEG analysis for automatic identification of abnormal signals. Objective To develop an integrated open-source EHR system for the management of the patients’ personal, clinical, and EEG data, and for automatic identification of abnormal EEG signals. Methods The core of the system is an EHR and telehealth service based on the OpenMRS platform. On top of that, we developed an intelligent component to automatically detect abnormal segments of EEG tests using machine learning algorithms, as well as a service to annotate and visualize abnormal segments in EEG signals. Finally, we evaluated the intelligent component and the integrated system using precision, recall, and accuracy metrics. Results The system allowed to manage patients’ information properly, store and manage the EEG tests recorded with a medical EEG device, and to detect abnormal segments of signals with a precision of 85.10%, a recall of 97.16%, and an accuracy of 99.92%. Conclusion Digital health is a multidisciplinary field of research in which artificial intelligence is playing a significant role in boosting traditional health services. Notably, the developed system could significantly reduce the time a neurologist spends in the reading of an EEG for the diagnosis of epilepsy, saving approximately 65–75% of the time consumed. It can be used in a telehealth environment. In this way, the availability and provision of diagnostic services for epilepsy management could be improved, especially in developing countries where the number of neurologists is low.
Collapse
Affiliation(s)
- Edward Molina
- Telematics Department, Universidad del Cauca, Popayán, Cauca, Colombia
| | | | | | - Diego M López
- Telematics Department, Universidad del Cauca, Popayán, Cauca, Colombia
| | | |
Collapse
|
21
|
Raghu S, Sriraam N, Gommer ED, Hilkman DMW, Temel Y, Rao SV, Hegde AS, Kubben PL. Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction. Clin Neurophysiol 2020; 131:1567-1578. [PMID: 32417698 DOI: 10.1016/j.clinph.2020.03.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/04/2020] [Accepted: 03/12/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
Collapse
Affiliation(s)
- S Raghu
- Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Natarajan Sriraam
- Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Erik D Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Danny M W Hilkman
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Pieter L Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| |
Collapse
|
22
|
George ST, Subathra M, Sairamya N, Susmitha L, Joel Premkumar M. Classification of epileptic EEG signals using PSO based artificial neural network and tunable-Q wavelet transform. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.02.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
|
23
|
Sharma R, Chopra K. EEG-based epileptic seizure detection using GPLV model and multi support vector machine. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2020. [DOI: 10.1080/02522667.2020.1715564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ruchi Sharma
- Departartment of Electrical and Electronics Engineering, GD Goenka University, Gurugram 122103, Haryana, India
| | - Khyati Chopra
- Departartment of Electrical and Electronics Engineering, GD Goenka University, Gurugram 122103, Haryana, India,
| |
Collapse
|
24
|
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]
|
25
|
Jebakumari VS, Saravanan DS, Devaraj D. Seizure detection in EGG signal with novel optimization algorithm for selecting optimal thresholded offset Gaussian feature. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101708] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
26
|
Diykh M, Li Y, Abdulla S. EEG sleep stages identification based on weighted undirected complex networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105116. [PMID: 31629158 DOI: 10.1016/j.cmpb.2019.105116] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 09/14/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. METHODS Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. RESULTS In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. CONCLUSIONS An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.
Collapse
Affiliation(s)
- Mohammed Diykh
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia; College of Education for Pure Science, University of Thi-Qar, Iraq.
| | - Yan Li
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia.
| | - Shahab Abdulla
- Open Access College, University of Southern Queensland, Australia.
| |
Collapse
|
27
|
Channel binary pattern based global-local spatial information fusion for motor imagery tasks. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
28
|
Sun C, Cui H, Zhou W, Nie W, Wang X, Yuan Q. Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning. Int J Neural Syst 2019; 29:1950021. [DOI: 10.1142/s0129065719500217] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.
Collapse
Affiliation(s)
- Chengfa Sun
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250101, P. R. China
| | - Weiwei Nie
- Shandong Provincial Qianfoshan Hospital, Shandong First Medical University, Jinan 250014, P. R. China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
| | - Qi Yuan
- Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China
| |
Collapse
|
29
|
Aydemir E, Tuncer T, Dogan S. A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Med Hypotheses 2019; 134:109519. [PMID: 31877443 DOI: 10.1016/j.mehy.2019.109519] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/28/2019] [Accepted: 12/07/2019] [Indexed: 11/24/2022]
Abstract
Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation.
Collapse
Affiliation(s)
- Emrah Aydemir
- Department of Computer Engineering, Engineering Faculty, Kirsehir Ahi Evran University, Kirsehir, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| |
Collapse
|
30
|
Zhan T, Fatmi SZ, Guraya S, Kassiri H. A Resource-Optimized VLSI Implementation of a Patient-Specific Seizure Detection Algorithm on a Custom-Made 2.2 cm 2 Wireless Device for Ambulatory Epilepsy Diagnostics. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:1175-1185. [PMID: 31634843 DOI: 10.1109/tbcas.2019.2948301] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A patient-specific epilepsy diagnostic solution in the form of a wireless wearable ambulatory device is presented. First, the design, VLSI implementation, and experimental validation of a resource-optimized machine learning algorithm for epilepsy seizure detection are described. Next, the development of a mini-PCB that integrates a low-power wireless data transceiver and a programmable processor for hosting the seizure detection algorithm is discussed. The algorithm uses only EEG signals from the frontal lobe electrodes while yielding a seizure detection sensitivity and specificity competitive to the standard full EEG systems. The experimental validation of the algorithm VLSI implementation proves the possibility of conducting accurate seizure detection using quickly-mountable dry-electrode headsets without the need for uncomfortable/painful through-hair electrodes or adhesive gels. Details of design and optimization of the algorithm, the VLSI implementation, and the mini-PCB development are presented and resource optimization techniques are discussed. The optimized implementation is uploaded on a low-power Microsemi Igloo FPGA, requires 1237 logic elements, consumes 110 μW dynamic power, and yields a minimum detection latency of 10.2 μs. The measurement results from the FPGA implementation on data from 23 patients (198 seizures in total) shows a seizure detection sensitivity and specificity of 92.5% and 80.1%, respectively. Comparison to the state of the art is presented from system integration, the VLSI implementation, and the wireless communication perspectives.
Collapse
|
31
|
Wu D, Wang Z, Huang H, Wang G, Liu J, Cai C, Xu W. Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG. INT J HUM ROBOT 2019. [DOI: 10.1142/s0219843619500166] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).
Collapse
Affiliation(s)
- Duanpo Wu
- School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, P. R. China
- Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Zhejiang, Hangzhou 310027, P. R. China
- Department of Algorithm, Hangzhou Neuro Science and Technology Co., Ltd., Room 2601, Huaye Building, Hangzhou 310051, P. R. China
| | - Zimeng Wang
- School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, P. R. China
| | - Hong Huang
- Pharmacy Department, Huaxi Second Hospital, No. 20, Section 3, Renmin South Road, Jinjiang District, Chengdu 610041, P. R. China
| | - Guangsheng Wang
- Department of Neurology, Shuyang People’s Hospital, Xuzhou Medical University, No. 9 Yingbin Avenue, Yucheng Town, Xuzhou 223000, P. R. China
| | - Junbiao Liu
- Department of Algorithm, Hangzhou Neuro Science and Technology Co., Ltd., Room 2601, Huaye Building, Hangzhou 310051, P. R. China
| | - Chenyi Cai
- Department of Algorithm, Hangzhou Neuro Science and Technology Co., Ltd., Room 2601, Huaye Building, Hangzhou 310051, P. R. China
| | - Weifeng Xu
- Department of Algorithm, Hangzhou Neuro Science and Technology Co., Ltd., Room 2601, Huaye Building, Hangzhou 310051, P. R. China
| |
Collapse
|
32
|
Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.028] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
33
|
Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04389-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
34
|
Kumar S, Sharma A, Tsunoda T. Brain wave classification using long short-term memory network based OPTICAL predictor. Sci Rep 2019; 9:9153. [PMID: 31235800 PMCID: PMC6591300 DOI: 10.1038/s41598-019-45605-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 06/07/2019] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL .
Collapse
Affiliation(s)
- Shiu Kumar
- The University of the South Pacific, Suva, Fiji. .,Fiji National University, Suva, Fiji.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia. .,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,The University of the South Pacific, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.,CREST, JST, Tokyo, 102-8666, Japan
| |
Collapse
|
35
|
Deriche M, Arafat S, Al-Insaif S, Siddiqui M. Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data. Ing Rech Biomed 2019. [DOI: 10.1016/j.irbm.2019.02.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
36
|
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]
|
37
|
Mahapatra AG, Singh B, Horio K, Wagatsuma H. MCA Based Epilepsy EEG Classification Using Time Frequency Domain Features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:3398-3401. [PMID: 30441117 DOI: 10.1109/embc.2018.8513017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented into analytical form by using Hilbert transform. Then features, parameter's ratio of bandwidth square, mean square frequency and fractional contributions to dominant frequency were extracted to discriminate epilepsy EEG by support vector machine (SVM). These features have shown classification results comparable to previous works.
Collapse
|
38
|
Lu Y, Ma Y, Chen C, Wang Y. Classification of single-channel EEG signals for epileptic seizures detection based on hybrid features. Technol Health Care 2018; 26:337-346. [PMID: 29710759 PMCID: PMC6004942 DOI: 10.3233/thc-174679] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Epilepsy is a common chronic neurological disorder of the brain. Clinically, epileptic seizures are usually detected via the continuous monitoring of electroencephalogram (EEG) signals by experienced neurophysiologists. OBJECTIVE In order to detect epileptic seizures automatically with a satisfactory precision, a new method is proposed which defines hybrid features that could characterize the epileptiform waves and classify single-channel EEG signals. METHODS The hybrid features consist of both the ones usually used in EEG signal analysis and the Kraskov entropy based on Hilbert-Huang Transform which is proposed for the first time. With the hybrid features, EEG signals are classified and the epileptic seizures are detected. RESULTS Three datasets are used for test on three binary-classification problems defined by clinical requirements for epileptic seizures detection. Experimental results show that the accuracy, sensitivity and specificity of the proposed methods outperform two state-of-the-art methods, especially on the databases containing signals from different sources. CONCLUSIONS The proposed method provides a new avenue to assist neurophysiologists in diagnosing epileptic seizures automatically and accurately.
Collapse
Affiliation(s)
- Yanan Lu
- Department of Electronic Engineering, Fudan University, Shanghai, China
| | - Yu Ma
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| | - Chen Chen
- Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
| | - Yuanyuan Wang
- Department of Electronic Engineering, Fudan University, Shanghai, China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China
| |
Collapse
|
39
|
Detti P, de Lara GZM, Bruni R, Pranzo M, Sarnari F, Vatti G. A Patient-Specific Approach for Short-Term Epileptic Seizures Prediction Through the Analysis of EEG Synchronization. IEEE Trans Biomed Eng 2018; 66:1494-1504. [PMID: 30296211 DOI: 10.1109/tbme.2018.2874716] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting about 65 millions individuals worldwide. OBJECTIVE This paper proposes a patient-specific approach for short-term prediction (i.e., within few minutes) of epileptic seizures. METHODS We use noninvasive EEG data, since the aim is exploring the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. Our approach is based on finding synchronization patterns in the EEG that allow to distinguish in real time preictal from interictal states. In practice, we develop easily computable functions over a graph model to capture the variations in the synchronization, and employ a classifier for identifying the preictal state. RESULTS We compare two state-of-the-art classification algorithms and a simple and computationally inexpensive threshold-based classifier developed ad hoc. Results on publicly available scalp EEG database and on scalp data of the patients of the Unit of Neurology and Neurophysiology at University of Siena show that this simple and computationally viable processing is able to highlight the changes in synchronization when a seizure is approaching. CONCLUSION AND SIGNIFICANCE The proposed approach, characterized by low computational requirements and by the use of noninvasive techniques, is a step toward the development of portable and wearable devices for real-life use.
Collapse
|
40
|
The earth mover's distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG. Biomed Eng Lett 2018; 8:373-382. [PMID: 30603222 DOI: 10.1007/s13534-018-0082-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 07/21/2018] [Accepted: 07/31/2018] [Indexed: 10/28/2022] Open
Abstract
Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.
Collapse
|
41
|
Fan M, Chou CA. Detecting Abnormal Pattern of Epileptic Seizures via Temporal Synchronization of EEG Signals. IEEE Trans Biomed Eng 2018; 66:601-608. [PMID: 29993518 DOI: 10.1109/tbme.2018.2850959] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Synchronization phenomena of epileptic electroencephalography (EEG) have long been studied. In this study, we aim at investigating the spatial-temporal synchronization pattern in epileptic human brains using the spectral graph theoretic features extracted from scalp EEG and developing an efficient multivariate approach for detecting seizure onsets in real time. METHODS A complex network model is used for representing the recurrence pattern of EEG signals, based on which the temporal synchronization patterns are quantified using the spectral graph theoretic features. Furthermore, a statistical control chart is applied to the extracted features overtime for monitoring the transits from normal to epileptic states in multivariate EEG systems. RESULTS Our method is tested on 23 patients from CHB-MIT Scalp EEG database. The results show that the graph theoretic feature yields a high sensitivity ( ∼ 98%) and low latency ( ∼ 6 s) on average, and seizure onsets in 18 patients are 100% detected. CONCLUSION Our approach validates the increased temporal synchronization in epileptic EEG and achieves a comparable detection performance to previous studies. SIGNIFICANCE We characterize the temporal synchronization patterns of epileptic EEG using spectral network metrics. In addition, we found significant changes in temporal synchronization in epileptic EEG, which enable a patient-specific approach for real-time seizure detection for personalized diagnosis and treatment.
Collapse
|
42
|
Kumar S, Sharma A. A new parameter tuning approach for enhanced motor imagery EEG signal classification. Med Biol Eng Comput 2018; 56:1861-1874. [PMID: 29616456 DOI: 10.1007/s11517-018-1821-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 03/19/2018] [Indexed: 12/13/2022]
Abstract
A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.
Collapse
Affiliation(s)
- Shiu Kumar
- Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Samabula, Fiji
- School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji
| | - Alok Sharma
- School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji.
- Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Brisbane, Australia.
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
| |
Collapse
|
43
|
Wang D, Ren D, Li K, Feng Y, Ma D, Yan X, Wang G. Epileptic Seizure Detection in Long-Term EEG Recordings by Using Wavelet-Based Directed Transfer Function. IEEE Trans Biomed Eng 2018; 65:2591-2599. [PMID: 29993489 DOI: 10.1109/tbme.2018.2809798] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
GOAL The accurate automatic detection of epileptic seizures is very important in long-term electroencephalogram (EEG) recordings. In this study, the wavelet decomposition and the directed transfer function (DTF) algorithm were combined to present a novel wavelet-based directed transfer function (WDTF) method for the patient-specific seizure detection. METHODS First, five subbands were extracted from 19-channel EEG signals by using wavelet decomposition in a sliding window. Second, the information flow characteristics of five subbands and full frequency band of EEG signals were calculated by the DTF method. The intensity of the outflow information was then used to reduce the feature dimensionality. Finally, all features were combined to identify interictal and ictal EEG segments by the support vector machine classifier. RESULTS By using fivefold cross validation, the proposed method had achieved excellent performance with the average accuracy of 99.4%, the average selectivity of 91.1%, the average sensitivity of 92.1%, the average specificity of 99.5%, and the average detection rate of 95.8%. CONCLUSION The WDTF method is able to enhance seizure detection results in long-term EEG recordings of focal epilepsy patients. SIGNIFICANCE This study may lead to the development of seizure detection system with high performance, thus reducing the workload of epileptologists and facilitating to take corresponding steps promptly after the seizure onset. The high-frequency activity in the epilepsy brain may be of great importance for investigating the pathological mechanism and treatment of seizure.
Collapse
|
44
|
|
45
|
Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.05.005] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
46
|
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:6849360. [PMID: 28706561 PMCID: PMC5494790 DOI: 10.1155/2017/6849360] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 05/21/2017] [Indexed: 11/18/2022]
Abstract
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.
Collapse
|