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Wang J, Sun M, Huang W. Unsupervised EEG-Based Seizure Anomaly Detection with Denoising Diffusion Probabilistic Models. Int J Neural Syst 2024; 34:2450047. [PMID: 38864575 DOI: 10.1142/s0129065724500473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
While many seizure detection methods have demonstrated great accuracy, their training necessitates a substantial volume of labeled data. To address this issue, we propose a novel method for unsupervised seizure anomaly detection called SAnoDDPM, which uses denoising diffusion probabilistic models (DDPM). We designed a novel pipeline that uses a variable lower bound on Markov chains to identify potential values that are unlikely to occur in anomalous data. The model is first trained on normal data, then anomalous data is input to the trained model. The model resamples the anomalous data and converts it to normal data. Finally, the presence of seizures can be determined by comparing the before and after data. Moreover, the input 2D spectrograms are encoded into vector-quantized representations, which enables powerful and efficient DDPM while maintaining its quality. Experimental comparisons on the publicly available datasets, CHB-MIT and TUH, show that our method delivers better results, significantly reduces inference time, and is suitable for deployment in a clinical environments. As far as we are aware, this is the first DDPM-based method for seizure anomaly detection. This novel approach significantly contributes to the progression of seizure detection algorithms, thereby augmenting their practicality in clinical settings.
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Affiliation(s)
- Jiale Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
| | - Mengxue Sun
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
| | - Wenhui Huang
- School of Information Science and Engineering, Shandong Normal University, Jinan, P. R. China
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Wu M, Peng H, Liu Z, Wang J. Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model. Int J Neural Syst 2024:2450051. [PMID: 39004932 DOI: 10.1142/s0129065724500515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.
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Affiliation(s)
- Min Wu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Zhicai Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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3
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Liu G, Tian L, Wen Y, Yu W, Zhou W. Cosine convolutional neural network and its application for seizure detection. Neural Netw 2024; 174:106267. [PMID: 38555723 DOI: 10.1016/j.neunet.2024.106267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Traditional convolutional neural networks (CNNs) often suffer from high memory consumption and redundancy in their kernel representations, leading to overfitting problems and limiting their application in real-time, low-power scenarios such as seizure detection systems. In this work, a novel cosine convolutional neural network (CosCNN), which replaces traditional kernels with the robust cosine kernel modulated by only two learnable factors, is presented, and its effectiveness is validated on the tasks of seizure detection. Meanwhile, based on the cosine lookup table and KL-divergence, an effective post-training quantization algorithm is proposed for CosCNN hardware implementation. With quantization, CosCNN can achieve a nearly 75% reduction in the memory cost with almost no accuracy loss. Moreover, we design a configurable cosine convolution accelerator on Field Programmable Gate Array (FPGA) and deploy the quantized CosCNN on Zedboard, proving the proposed seizure detection system can operate in real-time and low-power scenarios. Extensive experiments and comparisons were conducted using two publicly available epileptic EEG databases, the Bonn database and the CHB-MIT database. The results highlight the performance superiority of the CosCNN over traditional CNNs as well as other seizure detection methods.
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Affiliation(s)
- Guoyang Liu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Lan Tian
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Yiming Wen
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weize Yu
- School of Integrated Circuits, Shandong University, Jinan 250100, China
| | - Weidong Zhou
- School of Integrated Circuits, Shandong University, Jinan 250100, China.
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Liao J, Wang J, Zhan CA, Yang F. Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection. Phys Eng Sci Med 2024; 47:31-47. [PMID: 37747646 DOI: 10.1007/s13246-023-01340-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/14/2023] [Indexed: 09/26/2023]
Abstract
Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.
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Affiliation(s)
- Jiahui Liao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China
| | - Jun Wang
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chang'an A Zhan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
| | - Feng Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
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Jibon FA, Jamil Chowdhury AR, Miraz MH, Jin HH, Khandaker MU, Sultana S, Nur S, Siddiqui FH, Kamal AHM, Salman M, Youssef AAF. Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal. Digit Health 2024; 10:20552076241249874. [PMID: 38726217 PMCID: PMC11080778 DOI: 10.1177/20552076241249874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 05/12/2024] Open
Abstract
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.
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Affiliation(s)
- Ferdaus Anam Jibon
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - A. R. Jamil Chowdhury
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Mahadi Hasan Miraz
- Department of Management, Marketing and Digital Business, Faculty of Business, Curtin University Malaysia, Miri, Malaysia
| | - Hwang Ha Jin
- Department of Business Analytics, Sunway University, Bandar Sunway, Selangor, Malaysia
| | - Mayeen Uddin Khandaker
- Applied Physics and Radiation Technologies Group, CCDCU, School of Engineering and Technology, Sunway University, Bandar Sunway, Selangor, Malaysia
- Faculty of Graduate Studies, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka, Bangladesh
| | - Sajia Sultana
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Sifat Nur
- Department of Computer Science & Engineering, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh
| | - Fazlul Hasan Siddiqui
- Department of Computer Science & Engineering, Dhaka
University of Engineering & Technology (DUET), Gazipur, Dhaka, Bangladesh
| | - AHM Kamal
- Department of Computer Science & Engineering, Jatiya Kabi Kazi Nazrul Islam University (JKKNIU), Trishal, Mymensingh, Bangladesh
| | - Mohammad Salman
- College of Engineering and Technology, American University of the Middle East, Kuwait
| | - Ahmed A. F. Youssef
- College of Engineering and Technology, American University of the Middle East, Kuwait
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Dong C, Sun D. Brain network classification based on dynamic graph attention information bottleneck. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107913. [PMID: 37952340 DOI: 10.1016/j.cmpb.2023.107913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Graph neural networks (GNN) have demonstrated remarkable encoding capabilities in the context of brain network classification tasks. They excel at uncovering hidden static connections between brain states. However, brain network signals can be influenced by physiological traits and external variables during clinical detection, resulting in noisy brain graphs. Additionally, many existing algorithms for brain networks primarily focus on static topologies determined by threshold-based criteria, thereby overlooking the real-time variability in brain channel connectivity. These sources of noise and the persistence of static structures inevitably hinder the effective exchange of information during brain network computations. METHODS To address these challenges, we propose a novel framework called the dynamic graph attention information bottleneck (DGAIB). This framework is designed to dynamically enhance the input raw brain graph structure from the perspective of information theory and graph theory. First, we employ the Spearman function to construct a raw graph. Then, we use a graph information bottleneck (GIB) to optimize the internal graph connections by selectively masking redundant feature embeddings. Finally, we enhance the feature aggregation of each brain state by utilizing a graph attention network (GAT), which promotes improved information exchange among distinct brain regions within the model. These processed representations serve as input for subsequent classification tasks. EXPERIMENT AND RESULTS We systematically evaluated the robustness and generalizability of our proposed framework through a series of experiments. This evaluation included patient-specific experiments using the electroencephalography (EEG)-based CHB-MIT dataset and cross-patient experiments leveraging the functional magnetic resonance imaging (fMRI)-based ABIDE-I dataset from multiple perspectives.
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Affiliation(s)
- Changxu Dong
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Dengdi Sun
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Artificial Intelligence, Anhui University, Hefei 230601, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230026, China.
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Tripathi PM, Kumar A, Kumar M, Komaragiri RS. Automatic seizure detection and classification using super-resolution superlet transform and deep neural network -A preprocessing-less method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107680. [PMID: 37459774 DOI: 10.1016/j.cmpb.2023.107680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 08/29/2023]
Abstract
CONTEXT Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accurately with minimal human intervention. PROPOSED APPROACH This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method. METHODOLOGY SLT, a high-resolution time-frequency technique, converts EEG records into two-dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre-trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems. RESULTS The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure detection cases considered in this work. In the case of three and five-class classification problems, the proposed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events. CONCLUSION The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.
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Affiliation(s)
- Prashant Mani Tripathi
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
| | - Rama S Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
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Lih OS, Jahmunah V, Palmer EE, Barua PD, Dogan S, Tuncer T, García S, Molinari F, Acharya UR. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Comput Biol Med 2023; 164:107312. [PMID: 37597408 DOI: 10.1016/j.compbiomed.2023.107312] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.
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Affiliation(s)
- Oh Shu Lih
- Cogninet Australia, Sydney, NSW, 2010, Australia
| | - V Jahmunah
- School of Engineering, Nanyang Polytechnic, Singapore
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia
| | - Prabal D Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Salvador García
- Andalusian Institute of Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
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Statsenko Y, Babushkin V, Talako T, Kurbatova T, Smetanina D, Simiyu GL, Habuza T, Ismail F, Almansoori TM, Gorkom KNV, Szólics M, Hassan A, Ljubisavljevic M. Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach. Biomedicines 2023; 11:2370. [PMID: 37760815 PMCID: PMC10525492 DOI: 10.3390/biomedicines11092370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 09/29/2023] Open
Abstract
Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.
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Affiliation(s)
- Yauhen Statsenko
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Medical Imaging Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Vladimir Babushkin
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tatsiana Talako
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Oncohematology, Minsk Scientific and Practical Center for Surgery, Transplantology and Hematology, 220089 Minsk, Belarus
| | - Tetiana Kurbatova
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Darya Smetanina
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Gillian Lylian Simiyu
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tetiana Habuza
- Big Data Analytics Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Fatima Ismail
- Pediatric Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Taleb M. Almansoori
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Klaus N.-V. Gorkom
- Radiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Miklós Szólics
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
- Internal Medicine Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Ali Hassan
- Neurology Division, Medicine Department, Tawam Hospital, Al Ain P.O. Box 15258, United Arab Emirates
| | - Milos Ljubisavljevic
- Physiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates;
- Neuroscience Platform, ASPIRE Precision Medicine Research Institute Abu Dhabi, Al Ain P.O. Box 15551, United Arab Emirates
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Tian Z, Hu B, Si Y, Wang Q. Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier. Brain Sci 2023; 13:brainsci13050820. [PMID: 37239292 DOI: 10.3390/brainsci13050820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 04/28/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.
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Affiliation(s)
- Ziwei Tian
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 101408, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Bingliang Hu
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
| | - Yang Si
- Department of Neurology, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, Chengdu 610072, China
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Quan Wang
- Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China
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11
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Maher C, Yang Y, Truong ND, Wang C, Nikpour A, Kavehei O. Seizure detection with reduced electroencephalogram channels: research trends and outlook. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230022. [PMID: 37153360 PMCID: PMC10154941 DOI: 10.1098/rsos.230022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023]
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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Affiliation(s)
- Christina Maher
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Yikai Yang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia
| | - Armin Nikpour
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
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12
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Khan GH, Khan NA, Altaf MAB, Abbasi Q. A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:4112. [PMID: 37112452 PMCID: PMC10144298 DOI: 10.3390/s23084112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/11/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.
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Affiliation(s)
- Gul Hameed Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Nadeem Ahmad Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Muhammad Awais Bin Altaf
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
- Engineering and Design Department, Western Washington University, Bellingham, WA 98225, USA
| | - Qammer Abbasi
- Communications Sensing and Imaging Research Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
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13
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Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges. Diagnostics (Basel) 2023; 13:diagnostics13061058. [PMID: 36980366 PMCID: PMC10047386 DOI: 10.3390/diagnostics13061058] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 03/14/2023] Open
Abstract
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
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14
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Divya R, Shantha Selva Kumari R. Detection of Alzheimer’s disease from temporal lobe grey matter slices using 3D CNN. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2173548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- R. Divya
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India
| | - R. Shantha Selva Kumari
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India
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15
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Interactive local and global feature coupling for EEG-based epileptic seizure detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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16
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GNMF-based quadratic feature extraction in SSTFT domain for epileptic EEG detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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17
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He J, Yang L, Liu D, Song Z. Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree. Brain Sci 2022; 12:brainsci12091197. [PMID: 36138933 PMCID: PMC9497056 DOI: 10.3390/brainsci12091197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/16/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Epilepsy (Ep) is a chronic neural disease. The diagnosis of epilepsy depends on detailed seizure history and scalp electroencephalogram (EEG) examinations. The automatic recognition of epileptic EEG is an artificial intelligence application developed from machine learning (ML). Purpose: This study compares the classification effects of two kinds of classifiers by controlling the EEG data source and characteristic values. Method: All EEG data were collected by GSN HydroCel 256 leads and high-density EEG from Xiangya Third Hospital. This study used time-domain features (mean, kurtosis and skewness processed by empirical mode decomposition (EMD) and three IMFs), a frequency-domain feature (power spectrum density, PSD) and a non-linear feature (Shannon entropy). Support vector machine (SVM) and gradient-boosting decision tree (GBDT) classifiers were used to recognize epileptic EEG. Result: The result of the SVM classifier showed an accuracy of 72.00%, precision of 73.98%, and an F1_score of 82.28%. Meanwhile, the result of the GBDT classifier showed a sensitivity of 98.57%, precision of 89.13%, F1_score of 93.40%, and an AUC of 0.9119. Conclusion: The comparison of GBDT and SVM by controlling the variables of the feature values and parameters of a classifier is presented. GBDT obtained the better classification accuracy (90.00%) and F1_score (93.40%).
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Affiliation(s)
| | | | | | - Zhi Song
- Correspondence: ; Tel.: +86-13974814092
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18
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He J, Cui J, Zhang G, Xue M, Chu D, Zhao Y. Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103908] [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]
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19
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Interpretable seizure detection with signal temporal logic neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Ahmad I, Wang X, Zhu M, Wang C, Pi Y, Khan JA, Khan S, Samuel OW, Chen S, Li G. EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6486570. [PMID: 35755757 PMCID: PMC9232335 DOI: 10.1155/2022/6486570] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 12/21/2022]
Abstract
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.
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Affiliation(s)
- Ijaz Ahmad
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
| | - Mingxing Zhu
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen, China
| | - Cheng Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
| | - Yao Pi
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Javed Ali Khan
- Department of Software Engineering, University of Science and Technology, Bannu, Khyber Pakhtunkhwa, Pakistan
| | - Siyab Khan
- Institute of Computer Science and Information Technology, The University of Agriculture, Peshawar, Khyber Pakhtunkhwa, Pakistan
| | - Oluwarotimi Williams Samuel
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
| | - Shixiong Chen
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen, China
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21
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Guo Y, Jiang X, Tao L, Meng L, Dai C, Long X, Wan F, Zhang Y, van Dijk J, Aarts RM, Chen W, Chen C. Epileptic Seizure Detection by Cascading Isolation Forest-based Anomaly Screening and EasyEnsemble. IEEE Trans Neural Syst Rehabil Eng 2022; 30:915-924. [PMID: 35353703 DOI: 10.1109/tnsre.2022.3163503] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children's Hospital Boston - Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.
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22
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Cherian R, Kanaga EG. Theoretical and Methodological Analysis of EEG based Seizure Detection and Prediction: An Exhaustive Review. J Neurosci Methods 2022; 369:109483. [DOI: 10.1016/j.jneumeth.2022.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 02/07/2023]
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23
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Zhao Y, Dong C, Zhang G, Wang Y, Chen X, Jia W, Yuan Q, Xu F, Zheng Y. EEG-Based Seizure detection using linear graph convolution network with focal loss. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106277. [PMID: 34315015 DOI: 10.1016/j.cmpb.2021.106277] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Epilepsy is a clinical phenomenon caused by sudden abnormal and excessive discharge of brain neurons. It affects around 70 million people all over the world. Seizure detection from Electroencephalography (EEG) has achieved rapid development. However, existing methods often extract features from single channel EEG while ignoring the spatial relationship between different EEG channels. To fill this gap, a novel seizure detection model based on linear graph convolution network (LGCN) was proposed to enhance the feature embedding of raw EEG signals during seizure and non-seizure periods. METHOD Pearson correlation matrix of raw EEG signals was calculated to build the input graph of the graph neural network where the coefficients of the matrix models the spatial relations in EEG signals. The last softmax layer makes the final decision (seizure vs. non-seizure). In addition, focal loss was utilized to redefine the loss function of LGCN to deal with the data imbalance problem during seizure detection. RESULTS Experiments are conducted on the CHB-MIT dataset. The seizure detection accuracy, specificity, sensitivity, F1 and Auc are 99.30%, 98.82%, 99.43%, 98.73% and 98.57% respectively. CONCLUSIONS The proposed approach yields superior performance over the-state-of-the-art in seizure detection tasks on the CHB-MIT dataset. Our method works in an end-to-end manner and it does not need manually designed features. The ability to deal with imbalanced data is also attractive in real seizure detection scenarios where the duration of seizures is much shorter than the lasting time of non-seizure events.
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Affiliation(s)
- Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China.
| | - Changxu Dong
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Gaobo Zhang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Yaru Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Xin Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Weikuan Jia
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
| | - Qi Yuan
- School of Physics and Electronics, Shandong Normal University, Jinan 250358, P.R. China
| | - Fangzhou Xu
- School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P.R. China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P.R. China
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24
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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25
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Li M, Chen W. FFT-based deep feature learning method for EEG classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102492] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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26
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Li C, Zhou W, Liu G, Zhang Y, Geng M, Liu Z, Wang S, Shang W. Seizure Onset Detection Using Empirical Mode Decomposition and Common Spatial Pattern. IEEE Trans Neural Syst Rehabil Eng 2021; 29:458-467. [PMID: 33507872 DOI: 10.1109/tnsre.2021.3055276] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Automatic seizure onset detection plays an important role in epilepsy diagnosis. In this paper, a novel seizure onset detection method is proposed by combining empirical mode decomposition (EMD) of long-term scalp electroencephalogram (EEG) with common spatial pattern (CSP). First, wavelet transform (WT) and EMD are employed on EEG recordings respectively for filtering pre-processing and time-frequency decomposition. Then CSP is applied to reduce the dimension of multi-channel time-frequency representation, and the variance is extracted as the only feature. Afterwards, a support vector machine (SVM) group consisting of ten SVMs is served as a robust classifier. Finally, the post-processing is adopted to acquire a higher recognition rate and reduce the false detection rate. The results obtained from CHB-MIT database of 977 h scalp EEG recordings reveal that the proposed system can achieve a segment-based sensitivity of 97.34% with a specificity of 97.50% and an event-based sensitivity of 98.47% with a false detection rate of 0.63/h. This proposed detection system was also validated on a clinical scalp EEG database from the Second Hospital of Shandong University, and the system yielded a sensitivity of 93.67% and a specificity of 96.06%. At the event-based level, a sensitivity of 99.39% and a false detection rate of 0.64/h were obtained. Furthermore, this work showed that the CSP spatial filter was helpful to identify EEG channels involved in seizure onsets. These satisfactory results indicate that the proposed system may provide a reference for seizure onset detection in clinical applications.
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27
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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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28
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Supriya S, Siuly S, Wang H, Zhang Y. Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf Sci Syst 2020; 8:33. [PMID: 33088489 DOI: 10.1007/s13755-020-00129-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 10/06/2020] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5-29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.
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Affiliation(s)
- Supriya Supriya
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Hua Wang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
| | - Yanchun Zhang
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Footscray, Australia
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29
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Siddiqui MK, Morales-Menendez R, Huang X, Hussain N. A review of epileptic seizure detection using machine learning classifiers. Brain Inform 2020; 7:5. [PMID: 32451639 PMCID: PMC7248143 DOI: 10.1186/s40708-020-00105-1] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/09/2020] [Indexed: 01/13/2023] Open
Abstract
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers-'black-box' and 'non-black-box'. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.
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Affiliation(s)
- Mohammad Khubeb Siddiqui
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. E. Garza Sada 2501, Monterrey, Nuevo Leon Mexico
| | - Ruben Morales-Menendez
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. E. Garza Sada 2501, Monterrey, Nuevo Leon Mexico
| | - Xiaodi Huang
- School of Computing and Mathematics, Charles Sturt University, 2640 Albury, NSW Australia
| | - Nasir Hussain
- College of Applied Studies and Community Service, King Saud University, Riyadh, Kingdom of Saudi Arabia
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Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9235215] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.
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