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Feature Selection with Deep Belief Network for Epileptic seizure detection on EEG Signals

Computers, Materials & Continua 2023年第5期75卷 4101-4118页

作者： Srikanth Cherukuvada R.Kayalvizhi Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India

The term Epilepsy refers to a most commonly occurring brain disorder after a *** identification of incoming seizures significantly impacts the lives of people with *** detection of epileptic seizures(ES)has dramatically improved the life quality of the *** Electroencephalogram(EEG)related seizure detection mechanisms encountered several difficulties in *** EEGs are the non-stationary signal,and seizure patternswould changewith patients and recording ***,EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of *** intelligence(AI)methods in the domain of ES analysis use traditional deep learning(DL),and machine learning(ML)*** article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic seizure detection(OAOFS-DBNECD)technique using EEG *** primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of *** suggested OAOFS-DBNECD technique transforms the EEG signals *** format at the initial ***,the OAOFS technique selects an optimal subset of features using the preprocessed *** seizure classification,the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer(AEO)with a deep belief network(DBN)*** extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD *** comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other *** addition,the result of the suggested approach has been evaluated using the CHB-MIT database,and the findings demonstrate accuracy of 97.81%.These findings confirmed the best seizure categorization accuracy on the EEG data considered.

关键词： seizure detection EEG signals machine learning deep learning feature selection

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Automated Machine Learning for Epileptic seizure detection Based on EEG Signals

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Computers, Materials & Continua 2022年第10期73卷 1995-2011页

作者： Jian Liu Yipeng Du Xiang Wang Wuguang Yue Jim Feng University of Science and Technology Beijing Beijing100083China Hwa Create Co. LtdBeijing100193China Amphenol Global Interconnect Systems San JoseCA 95131US

Epilepsy is a common neurological disease and severely affects the daily life of *** automatic detection and diagnosis system of epilepsy based on electroencephalogram(EEG)is of great significance to help patients with epilepsy return to normal *** the development of deep learning technology and the increase in the amount of EEG data,the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted ***,the neural architecture design for epilepsy EEG analysis is time-consuming and laborious,and the designed structure is difficult to adapt to the changing EEG collection environment,which limits the application of the epilepsy EEG automatic detection *** this paper,we explore the possibility of Automated Machine Learning(AutoML)playing a role in the task of epilepsy EEG *** apply the neural architecture search(NAS)algorithm in the AutoKeras platform to design the model for epilepsy EEG analysis and utilize feature interpretability methods to ensure the reliability of the searched *** experimental results show that the model obtained through NAS outperforms the baseline model in *** searched model improves classification accuracy,F1-score and Cohen’s kappa coefficient by 7.68%,7.82%and 9.60%respectively than the baseline ***,NASbased model is capable of extracting EEG features related to seizures for classification.

关键词： Deep learning automated machine learning EEG seizure detection

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Fuzzy-Based Automatic Epileptic seizure detection Framework

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Computers, Materials & Continua 2022年第3期70卷 5601-5630页

作者： Aayesha Muhammad Bilal Qureshi Muhammad Afzaal Muhammad Shuaib Qureshi Jeonghwan Gwak Shaheed Zulfikar Ali Bhutto Institute of Science and Technology IslamabadPakistan Department of Computer Science&IT University of Lakki MarwatKPKPakistan Stockholm University StockholmSweden Department of Computer Science School of Arts and SciencesUniversity of Central AsiaKyrgyz Republic Department of Software Korea National University of TransportationChungju27469Korea Department of Biomedical Engineering Korea National University of TransportationChungju27469Korea Department of AI Robotics Engineering Korea National University of TransportationChungju27469Korea Department of IT&Energy Convergence(BK21 FOUR) Korea National University of TransportationChungju27469Korea

detection of epileptic seizures on the basis of Electroencephalogram(EEG)recordings is a challenging task due to the complex,non-stationary and non-linear nature of these biomedical *** the existing literature,a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning *** characterizing features of epileptic and non-epileptic EEG signals overlap;therefore,it requires that analysis of signals must be performed from diverse *** studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG *** pose the challenge mentioned above,in this paper,a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy *** proposed work extracts pattern features along with time-domain,frequencydomain,and non-linear analysis of *** applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic *** empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG *** shows significant accuracy of 98%to 100%for normal *** classification cases while for three class classification of normal ***-ictal *** accuracy reaches to above 97.5%.The obtained results for ten classification cases(including normal,seizure or ictal,and seizure-free or inter-ictal classes)prove the superior performance of proposed work as compared to other state-of-the-art counterparts.

关键词： Medical image processing electroencephalography machine learning fuzzy system models seizure detection epileptic seizure virtualization

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Automatic seizure detection with different time delays using SDFT and time-domain feature extraction

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The Journal of Biomedical Research 2022年第1期36卷 58-67页

作者： Amal S.Abdulhussien Ahmad T.AbdulSaddaa Kamran Iqbal Communication Technical Engineering College Al-Furat Al-Awsat Technical UniversityAl-Najaf 54001Iraq Department of System Engineering University of Arkansas at Little RockLittle RockAR 72204USA.

Automatic seizure detection is important for fast detection of the seizure because the way that the expert denotes and searches for seizure in the long signal takes time. The most common way to detect seizures automatically is to use an electroencephalogram(EEG). Many studies have used feature extraction that needs time for calculation. In this study, sliding discrete Fourier transform(SDFT) was applied for conversion to a frequency domain without using a window, which was compared with using window for feature selection. SDFT was calculated for each time series sample directly without any delay by using a simple infinite impulse response(IIR)structure. The EEG database of Bonn University was used to test the proposed method, and two cases were defined to examine a two-classifier feedforward neural network and an adaptive network-based fuzzy inference system. Results revealed that the maximum accuracies were 93% without delay and 99.8% with a one-second delay. This delay accrued because the average was taken for the results with a one-second window.

关键词： EEG seizure detection machine learning sliding discrete Fourier transform

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An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals

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The Journal of Biomedical Research 2020年第3期34卷 191-204页

作者： Dattaprasad Torse Veena Desai Rajashri Khanai Department of Electronics and Communication Engineering KLS Gogte Institute of TechnologyBelagaviKarnataka 590008India Department of Electronics and Communication Engineering KLE Dr.M.S.Sheshgiri College of Engineering and TechnologyBelagaviKarnataka 590008India

The detection of seizure onset and events using electroencephalogram(EEG) signals are important tasks in epilepsy *** literature available on seizure detection has discussed the implementation of advanced signal processing algorithms using tools accessed over the ***,seizure monitoring application needs near sensor processing due to privacy and latency *** this paper,a real time seizure detection system has been implemented using an embedded *** proposed system is based on ensemble empirical mode decomposition(EEMD) and tunable-Q wavelet transform(TQWT) *** analysis and classification of non-stationary EEG signals require the wavelet transform with high ***,direct use of TQWT increases the computational complexity of feature extraction from multivariate EEG *** this paper,the first step is to process the signal by using EEMD to obtain 8 intrinsic mode functions(IMFs).The Kraskov(KraEn),sample(SampEn),and permutation(PermEn) entropy features of IMFs are extracted and based on optimum values,and 4 IMFs are decomposed using ***,centered correntropy(CenCorrEn) features of the 1^(st)and 16^(th) sub-band of TQWT have been used as classifier *** performance of multilayer perceptron neural networks(MLPNN),least squares support vector machine(LSSVM),and random forest(RF) classifiers has been tested on the multichannel EEG data recorded from a local *** RF classifier has produced the highest accuracy of 96.2% in classifying the *** proposed scheme has been employed in developing an embedded seizure detection system to assist neurologists in making seizure diagnostic decisions.

关键词： seizure detection electroencephalogram ensemble empirical mode decomposition intrinsic mode function tunable-Q wavelet transform

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Transfer-Learning for Automated seizure detection Based on Electric Field Encephalography Reconstructed Signal

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Communications and Network 2020年第4期12卷 174-198页

作者： Gefei Zhu Cranbrook Schools Bloomfield Hills USA

Building an automatic seizure onset prediction model based on multi-channel electroencephalography (EEG) signals has been a hot topic in computer science and neuroscience field for a long time. In this research, we collect EEG data from different epilepsy patients and EEG devices and reconstruct and combine the EEG signals using an innovative electric field encephalography (EFEG) method, which establishes a virtual electric field vector, enabling extraction of electric field components and increasing detection accuracy compared to the conventional method. We extract a number of important features from the reconstructed signals and pass them through an ensemble model based on support vector machine (SVM), Random Forest (RF), and deep neural network (DNN) classifiers. By applying this EFEG channel combination method, we can achieve the highest detection accuracy at 87% which is 6% to 17% higher than the conventional channel averaging combination method. Meanwhile, to reduce the potential overfitting problem caused by DNN models on a small dataset and limited training patient, we ensemble the DNN model with two “weaker” classifiers to ensure the best performance in model transferring for different patients. Based on these methods, we can achieve the highest detection accuracy at 82% on a new patient using a different EEG device. Thus, we believe our method has good potential to be applied on different commercial and clinical devices.

关键词： EEG seizure detection Transfer Learning Deep Neural Network Electric Field Encephalography

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seizure detection using earth movers' distance and SVM in intracranial EEG

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Journal of Measurement Science and Instrumentation 2014年第3期5卷 94-102页

作者：王芸吴琦周卫东袁莎莎袁琦山东大学信息科学与工程学院山东济南250100 山东大学苏州研究院江苏苏州215123 山东大学齐鲁医院山东济南250100

seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

关键词： electroencephalograph (EEG)signals earth movers' distance (EMD) EMD-L1 support vector machine(SVM) wavelet decomposition seizure detection

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A bidirectional brain-computer interface for effective epilepsy control

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第10期15卷 839-847页

作者： Yu QI Fei-qiang MA Ting-ting GE Yue-ming WANG Jun-ming ZHU Jian-min ZHANG Xiao-xiang ZHENG Zhao-hui WU Qiushi Academy for Advanced Studies Zhejiang University Department of Computer Science Zhejiang University Second Affiliated Hospital of Zhejiang University College of Medicine College of Biomedical Engineering and Instrument Science Zhejiang University

Brain-computer interfaces(BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurostimulation can be greatly reduced. In this paper, a new BCI-based responsive stimulation system is proposed. With an efficient morphology-based seizure detector, seizure events can be identified in the early stages which trigger electrical stimulations to be sent to the cortex of the brain. The proposed system was tested on rats with penicillin-induced epileptic seizures. Online experiments show that 83% of the seizures could be detected successfully with a short average time delay of 3.11 s. With the therapy of the BCI-based seizure control system, most seizures were suppressed within 10 s. Compared with the control group, the average seizure duration was reduced by 30.7%. Therefore, the proposed system can control epileptic seizures effectively and has potential in clinical applications.

关键词： Brain-computer interface Epilepsy seizure detection Responsive neurostimulation

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Utilization of quantitative electroencephalogram in China:an online questionnaire survey

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Acta Epileptologica 2022年第3期5卷 194-200页

作者： Jiayin Li Xiangyi Zhu Shengjie Pan Yeting Lu Xiaowei Hu Department of Neurology The First Affiliated Hospital of Soochow UniversitySuzhou 215006China

Background:Quantitative electroencephalogram(QEEG)is a tool that uses a computer to analyze brain activity monitored by electroencephalogram(EEG)according to measurements such as frequency,amplitude,and *** purpose of this study was to understand the current situation of QEEG utilization in China and further compare the situations among different regions and different levels of ***:An online questionnaire comprising 14 questions was *** description and analysis were made for the results of the questionnaire ***:A total of 158 people from 134 medical institutions participated in the *** participants came from 21 provinces,accounting for 61.76%(21/34)of the 34 provincial administrative regions in *** Eastern China region accounted for 66.42%(89/134)of all the medical institutions that participated in this *** the institutions surveyed,QEEG was routinely used in only 23.88%(32/134)of *** the medical institutions in which QEEG was routinely used,87.50%(28/32)of them were 3A-grade *** the institutions with routine use of QEEG,56.25%(18/32)were affiliated hospitals of medical *** was a significant difference in the utilization of QEEG between the 3A-grade and non-3A-grade hospitals(P=0.040)and between the hospitals affiliated to medical schools and those non-affiliated to medical schools(P=0.020).Conclusions:The utilization of QEEG is still limited in *** are differences in the use of QEEG among different hospitals and regions.

关键词： Quantitative electroencephalogram Continuous electroencephalogram Questionnaire online seizure detection

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Automatic detection of seizures Using Extreme Learning Machine with a Single Feature

Automatic Detection of Seizures Using Extreme Learning Machi...

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第37届中国控制会议

作者： Yingmei Qin Chunxiao Han Meili Lu Ruofan Wang Li Yang Yanqiu Che IEEE Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical EngineeringTianjin University of Technology and Education School of Information Technology Engineering Tianjin University of Technology and Education

Automatic seizure detection is of great importance in clinical practice of epilepsy. This paper presents a classification system based on discrete wavelet transform（DWT） and the extreme learning machine（ELM） for epileptic seizure detection by distinguishing ictal and interictal electroencephalogram（EEG） signals. The original EEG signal is first decomposed by Daubechies order 4 wavelet into several sub-bands. Then, standard deviation, log of amplitude, and quartiles are calculated for the original and decomposed signals to construct feature vectors. Different combination of these features are fed into ELM and support vector machine（SVM）. After comparing different combination strategies, we find that, using ELM, even with a single feature（standard deviation） from a single sub-band signal（4-8 Hz）, one can obtain a satisfactory classification result, which remarkably reduce the computational complexity and make the detection system more practical.

关键词： seizure detection EEG Extreme Learning Machine(ELM) Discrete Wavelet Transform(DWT)

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