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Epileptic Seizure Classification Using Neural Networks with 14 Features [chapter]

Rui P. Costa, Pedro Oliveira, Guilherme Rodrigues, Bruno Leitão, António Dourado
Lecture Notes in Computer Science  
The neural networks use 14 features (extracted from EEG) in order to classify the brain state into one of four possible epileptic behaviors: inter-ictal, pre-ictal, ictal and pos-ictal.  ...  We concluded that with the 14 features and using the data of a single patient results in a classification accuracy of 99%, while using a network trained for multiple patients an accuracy of 98% is achieved  ...  These neural networks cover a wide spectrum of the available neural network approaches, allowing us to gather a good knowledge about the use of neural networks in the Epileptic Seizure Detection problem  ... 
doi:10.1007/978-3-540-85565-1_35 fatcat:g3wxuhg5avgrfmdt2ngghatyxy

Epileptic Seizure Prediction [chapter]

Shaik Jakeer Hussain, Gurajapu Raja Sumant
2020 Epilepsy [Working Title]  
Neural Networks.  ...  This study will use various signal processing methods to extract features by studying the pre-ictal and inter-ictal periods, localize the source and then finally predict epilepsy with the help of Artificial  ...  Epileptic seizure prediction using wavelet transforms and neural networks Feature extraction is done using DWT.  ... 
doi:10.5772/intechopen.94005 fatcat:mdrtyfqwffhpfpdnssl26m53fy


2012 International journal of computer and communication technology  
It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system.Two different types of neural networks, namely, Elman and probabilistic neural  ...  ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks.  ...  Artificial Neural Network (ANN) has been used for seizure related EEG recognition.  ... 
doi:10.47893/ijcct.2012.1149 fatcat:3cqv47keubdbbocs5ymivsued4

Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification

Yunyuan Gao, Bo Gao, Qiang Chen, Jia Liu, Yingchun Zhang
2020 Frontiers in Neurology  
For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.  ...  extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure).  ...  Epileptic EEG Signal Classification Here, epileptic EEG signal classification (EESC) is used for classifying four different epileptic states by using deep convolutional neural networks (DCNNs) and transfer  ... 
doi:10.3389/fneur.2020.00375 pmid:32528398 pmcid:PMC7257380 fatcat:o254cir4vzb6ncmi5bxjwiyfze

A Time-Frequency Based Method for the Detection of Epileptic Seizures in EEG Recordings

Alexandros T. Tzallas, Markos G. Tsipouras, Dimitrios I. Fotiadis
2007 Computer-Based Medical Systems (CBMS), Proceedings of the IEEE Symposium on  
Those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments (existence of epileptic seizure or not).  ...  A novel three-stage method for the analysis of electroencephalographic (EEG) signals, concerning epileptic seizures, is proposed.  ...  Classification The calculated features are fed into a feed-forward artificial neural network (ANN).  ... 
doi:10.1109/cbms.2007.17 dblp:conf/cbms/TzallasTF07 fatcat:3h57optojrcvpps4cvbrltwqgy

Epileptic State Detection: Pre-ictal, Inter-ictal, Ictal

Apdullah Yayik, Esen Yildirim, Yakup Kutlu, Serdar Yildirim
2015 International Journal of Intelligent Systems and Applications in Engineering  
As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.  ...  Neural Network classifier is used to classify these three classes.  ...  The numbers of points are calculated for each region and used as a feature vector. Classification Multi-Layer Neural Network Classifier ANNs are inspired by biological neural networks.  ... 
doi:10.18201/ijisae.14531 fatcat:7kwfq3xdnzd2tpyze7jqmyo77q

Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models [article]

Tennison Liu, Nhan Duy Truong, Armin Nikpour, Luping Zhou, Omid Kavehei
2020 arXiv   pre-print
Hybrid bilinear models based on two types of feature extractors, namely Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained using Short-Time Fourier Transform (STFT)  ...  This paper proposes a novel hybrid bilinear deep learning network with an application in the clinical procedures of epilepsy classification diagnosis, where the use of surface electroencephalogram (sEEG  ...  The topology of the CNN used in this study is highlighted in Fig. 2a . 2) Recurrent Neural Network: Recurrent Neural Networks (RNN) were designed to work with sequence prediction problems, with Long-Short  ... 
arXiv:2001.06282v1 fatcat:fxqcqwvcwvhk5bjcwga7w7cicm

Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

Laura Gagliano, Elie Bou Assi, Dang K. Nguyen, Mohamad Sawan
2019 Scientific Reports  
Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.  ...  This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks.  ...  As a follow-up, this study attempted to improve the classification performance of neural networks based on bispectral features using the same canine database with a different artificial neural network  ... 
doi:10.1038/s41598-019-52152-2 pmid:31666621 pmcid:PMC6821856 fatcat:63mv5lizyzgb7jicwl5j6nvkz4

Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks

Ateke Goshvarpour, Hossein Ebrahimnezhad, Atefeh Goshvarpour
2013 International Journal of Information Engineering and Electronic Business  
The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy  ...  Ocak [16] introduced detection of the epileptic seizures using discrete wavelet transform and approximation entropy.  ...  Probabilistic neural networks (PNN) can be used for Figure5.Probabilistic Neural Network architecture [31] . classification problems.  ... 
doi:10.5815/ijieeb.2013.01.07 fatcat:xomvhr644ndmxfelxojgsxaiku

EEG Subband Analysis using Approximate Entropy for the Detection of Epilepsy

G.R Kiranmayi, V Udayashankara
2014 IOSR Journal of Computer Engineering  
For both cases artificial neural networks with back propagation training are used as classifiers.  ...  The proposed method involves ApEn measured from EEG subbands applied as features to an artificial neural network (ANN) classifier.  ...  In the present work, a three layer feed-forward back propagation neural network is trained with ApEn feature vectors to detect and classify normal, inter ictal and ictal EEGs.  ... 
doi:10.9790/0661-16562127 fatcat:6l44b7rddve2li3fwcvmqvii4a

Epileptic Seizure: Classification Using Autoregression Features

Rajendran T, Sridhar K P, Vidhupriya P, Gayathri N, Anitha T
2021 International Journal of Current Research and Review  
Objective: To develop different autoregression feature extraction algorithms for identifying accurate features in the epileptic seizure EEG signals for the neural network-based classification.  ...  Different mental tasks are considered here to verify the proposed Probabilistic Neural Network-based Epileptic Lobe Seizure classifier.  ...  Louis Korczowski, Ph.D., and his team, GIPSA-lab, University of Grenoble-Alpes, France, for their P300 BCI (bi2014a) EEG open-access dataset which is used for normal brain activity classification in this  ... 
doi:10.31782/ijcrr.2021.13429 fatcat:3muca4rgi5ccfoaakcynqfc5dy

Minimum Feature Selection for Epileptic Seizure Classification using Wavelet-based Feature Extraction and a Fuzzy Neural Network

Sang-Hong Lee, Joon S. Lim
2014 Applied Mathematics & Information Sciences  
This paper proposes a method that uses a wavelet transform (WT) and a fuzzy neural network to select the minimum number of features for classifying normal signals and epileptic seizure signals from the  ...  We obtained 32 minimum features with the highest accuracy from the 40 initial features by using a non-overlap area distribution measurement method based on a neural network with weighted fuzzy membership  ...  Neural network with weighted fuzzy membership function (NEWFM) A neural network with a weighted fuzzy membership function (NEWFM) was used to select the minimum features for classifying normal signals  ... 
doi:10.12785/amis/080344 fatcat:uagaqa6xubhczd257qh3quc3a4

Epileptic Seizure Detection Using a Convolutional Neural Network [chapter]

Bassem Bouaziz, Lotfi Chaari, Hadj Batatia, Antonio Quintero-Rincón
2019 Drug Delivery Systems: Advanced Technologies Potentially Applicable in Personalised Treatment  
This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification.  ...  Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.  ...  neural network.  ... 
doi:10.1007/978-3-030-11800-6_9 fatcat:vu4fmzhiozg7lac3vldv7furuu

Detecting Epileptic Seizures from EEG Data using Neural Networks [article]

Siddharth Pramod, Adam Page, Tinoosh Mohsenin, Tim Oates
2019 arXiv   pre-print
We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG).  ...  The input to the neural network is a 126 feature vector containing 9 features for each of the 14 EEG channels obtained over 1-second, non-overlapping windows.  ...  One such application is the detection of epileptic seizures using electroencephalography (EEG) .  ... 
arXiv:1412.6502v6 fatcat:4yxaowex5jhsrle4nbjlxbztui

A Survey on Different Techniques for Epilepsy Seizures Detection in EEG

C.V Banupriya
2018 International Journal for Research in Applied Science and Engineering Technology  
Both these two feature extraction methods are apply to the input of machine learning classification algorithms such as some Neural Network algorithms, Support Vector Machines (SVM) and k-Means clustering  ...  An electroencephalogram (EEG) is a test out used to evaluate the electrical activity in the brain, and is widely used in the detection and study of epileptic seizures.  ...  For the regular classification of seizures a process is offered as well as achieved a classification rate of 92.3% by resources of a neural network with a single hidden unit as a classifier.  ... 
doi:10.22214/ijraset.2018.1306 fatcat:fgl67ofxvrf2ln3uzl3zwqj35e
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