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Awais M, Belhaouari SB, Kassoul K. Graphical Insight: Revolutionizing Seizure Detection with EEG Representation. Biomedicines 2024; 12:1283. [PMID: 38927490 PMCID: PMC11201274 DOI: 10.3390/biomedicines12061283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.
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Affiliation(s)
- Muhammad Awais
- Department of Creative Technologies, Air University, Islamabad 44000, Pakistan;
| | - Samir Brahim Belhaouari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha 5825, Qatar
| | - Khelil Kassoul
- Geneva School of Business Administration, University of Applied Sciences Western Switzerland, HES-SO, 1227 Geneva, Switzerland
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Ali E, Angelova M, Karmakar C. Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives. ROYAL SOCIETY OPEN SCIENCE 2024; 11:230601. [PMID: 39076791 PMCID: PMC11286169 DOI: 10.1098/rsos.230601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/23/2023] [Accepted: 03/28/2024] [Indexed: 07/31/2024]
Abstract
Epilepsy is a life-threatening neurological condition. Manual detection of epileptic seizures (ES) is laborious and burdensome. Machine learning techniques applied to electroencephalography (EEG) signals are widely used for automatic seizure detection. Some key factors are worth considering for the real-world applicability of such systems: (i) continuous EEG data typically has a higher class imbalance; (ii) higher variability across subjects is present in physiological signals such as EEG; and (iii) seizure event detection is more practical than random segment detection. Most prior studies failed to address these crucial factors altogether for seizure detection. In this study, we intend to investigate a generalized cross-subject seizure event detection system using the continuous EEG signals from the CHB-MIT dataset that considers all these overlooked aspects. A 5-second non-overlapping window is used to extract 92 features from 22 EEG channels; however, the most significant 32 features from each channel are used in experimentation. Seizure classification is done using a Random Forest (RF) classifier for segment detection, followed by a post-processing method used for event detection. Adopting all the above-mentioned essential aspects, the proposed event detection system achieved 72.63% and 75.34% sensitivity for subject-wise 5-fold and leave-one-out analyses, respectively. This study presents the real-world scenario for ES event detectors and furthers the understanding of such detection systems.
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Affiliation(s)
- Emran Ali
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
| | - Maia Angelova
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
- Aston Digital Futures Institute, EPS, Aston University, Birmingham, UK
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Melbourne Burwood Campus, Melbourne, Victoria3125, Australia
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Golla SK, Maloji S. A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3769. [PMID: 37740655 DOI: 10.1002/cnm.3769] [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: 04/05/2023] [Revised: 08/04/2023] [Accepted: 08/18/2023] [Indexed: 09/24/2023]
Abstract
Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time-consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non-linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time-frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE-GTRN-LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE-based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure-affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE-GTRN-LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state-of-the-art techniques.
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Affiliation(s)
- Sandhya Kumari Golla
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India
| | - Suman Maloji
- Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, India
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Yuan R, Tiw PJ, Cai L, Yang Z, Liu C, Zhang T, Ge C, Huang R, Yang Y. A neuromorphic physiological signal processing system based on VO 2 memristor for next-generation human-machine interface. Nat Commun 2023; 14:3695. [PMID: 37344448 DOI: 10.1038/s41467-023-39430-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/08/2023] [Indexed: 06/23/2023] Open
Abstract
Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and health-related information. However, the explosion of physiological signal data presents challenges for traditional systems. Here, we propose a highly efficient neuromorphic physiological signal processing system based on VO2 memristors. The volatile and positive/negative symmetric threshold switching characteristics of VO2 memristors are leveraged to construct a sparse-spiking yet high-fidelity asynchronous spike encoder for physiological signals. Besides, the dynamical behavior of VO2 memristors is utilized in compact Leaky Integrate and Fire (LIF) and Adaptive-LIF (ALIF) neurons, which are incorporated into a decision-making Long short-term memory Spiking Neural Network. The system demonstrates superior computing capabilities, needing only small-sized LSNNs to attain high accuracies of 95.83% and 99.79% in arrhythmia classification and epileptic seizure detection, respectively. This work highlights the potential of memristors in constructing efficient neuromorphic physiological signal processing systems and promoting next-generation human-machine interfaces.
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Affiliation(s)
- Rui Yuan
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Pek Jun Tiw
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Lei Cai
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Zhiyu Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Teng Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Ru Huang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yuchao Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, Beijing, 102206, China.
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Peketi S, Dhok SB. Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition. Brain Sci 2023; 13:brainsci13020315. [PMID: 36831857 PMCID: PMC9954262 DOI: 10.3390/brainsci13020315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.
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Wu W, Ma L, Lian B, Cai W, Zhao X. Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection. BIOSENSORS 2022; 12:1087. [PMID: 36551054 PMCID: PMC9775005 DOI: 10.3390/bios12121087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects' data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.
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Affiliation(s)
- Wei Wu
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Longhua Ma
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Bin Lian
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Weiming Cai
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Xianghong Zhao
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
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Huang X, Sun X, Zhang L, Zhu T, Yang H, Xiong Q, Feng L. A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15110. [PMID: 36429845 PMCID: PMC9690147 DOI: 10.3390/ijerph192215110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/12/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses. The lack of model interpretability also impedes the wider application and deeper understanding of ML methods in epilepsy detection. In this research, a novel feature extraction method based on an autoencoder (AE) is proposed in the time domain. The architecture and mechanism are elaborated. In this method, specified features are defined and calculated on the basis of signal reconstruction quantification of the AE. The EEG recognition is performed to validate the effectiveness of the proposed detection method, and the prediction accuracy reached 97%. To further investigate the superiority of the proposed AE-based feature extraction method, a widely used feature extraction method, PCA, is allocated for comparison. In order to understand the underlying working mechanism, permutation importance and SHapley Additive exPlanations (SHAP) are conducted for model interpretability, and the results further confirm the reasonability and effectiveness of the extracted features by AE reconstruction. With high computing efficiency in the time domain and an extensively satisfactory accuracy, the proposed epilepsy detection method exhibits great superiority and potential in almost real-time and automatic epilepsy monitoring.
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Affiliation(s)
- Xiaojie Huang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- Institute of Biopharmaceuticals, Anhui Medical University, Hefei 230032, China
| | - Xiangtao Sun
- Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
| | - Lijun Zhang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- Institute of Biopharmaceuticals, Anhui Medical University, Hefei 230032, China
| | - Tong Zhu
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- Institute of Biopharmaceuticals, Anhui Medical University, Hefei 230032, China
| | - Hao Yang
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- Institute of Biopharmaceuticals, Anhui Medical University, Hefei 230032, China
| | - Qingsong Xiong
- Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
| | - Lijie Feng
- School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, China
- Institute of Biopharmaceuticals, Anhui Medical University, Hefei 230032, China
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