IMPROVED ARTIFICIAL NEURAL NETWORK FOR EPILEPTIC SEIZURES DETECTION

2021 ◽  
pp. 2150045
Author(s):  
YASMINE BENCHAIB
Keyword(s):  
Classification Accuracy ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Significant Information ◽  
Relief Algorithm ◽  
Electroencephalogram Eeg ◽  
Interesting Alternative

Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

scholarly journals A New Signal Processing Approach for Discrimination of EEG Recordings

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 155-168 ◽  
Author(s):  
Hossein Hassani ◽  
Mohammad Yeganegi ◽  
Emmanuel Silva
Keyword(s):  
Principal Component Analysis ◽  
Brain Activity ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Eeg Recordings ◽  
Improving Accuracy ◽  
Electroencephalogram Eeg

Classifying brain activities based on electroencephalogram (EEG) signals is one of the important applications of time series discriminant analysis for diagnosing brain disorders. In this paper, we introduce a new method based on the Singular Spectrum Analysis (SSA) technique for classifying brain activity based on EEG signals via an application into a benchmark dataset for epileptic study with five categories, consisting of 100 EEG recordings per category. The results from the SSA based approach are xcompared with those from discrete wavelet transform before proposing a hybrid SSA and principal component analysis based approach for improving accuracy levels further.

Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients

2005 ◽  
Vol 36 (3) ◽  
pp. 188-193 ◽  
Author(s):  
Naoto Burioka ◽  
Germaine Cornélissen ◽  
Yoshihiro Maegaki ◽  
Franz Halberg ◽  
Daniel T. Kaplan ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Surrogate Data ◽  
Absence Epilepsy ◽  
Eeg Signals ◽  
Eyes Closed ◽  
The Mean ◽  
Electroencephalogram Eeg

The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (± SD) ApEn in EEG was 0.83 ± 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 ± 0.05) than during seizure-free intervals (0.80 ± 0.13) (P<0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.

scholarly journals Epilepsy Detection Using DWT Based Hurst Exponent and SVM, K-NN Classifiers

2018 ◽  
Vol 19 (4) ◽  
pp. 311-319 ◽  
Author(s):  
Ashok Sharmila ◽  
Saiby Madan ◽  
Kajri Srivastava
Keyword(s):  
Wavelet Transform ◽  
Healthy Subject ◽  
Classification Accuracy ◽  
Hurst Exponent ◽  
Sensory System ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eyes Open ◽  
Electroencephalogram Eeg

Abstract Epilepsy is a typical neurological issue which influence the focal sensory system and can make individuals have seizure. It can be surveyed by electroencephalogram (EEG). A wavelet based HURST EXPONENT strategy is displayed for the analysis of epilepsy. This strategy deals with the nonlinear analysis of EEG signals. Discrete wavelet transform is used to disintegrate the original EEG signal into specific subbands. The hurst exponent of different sub-bands is employed and then fed into two classifiers, namely SVM and KNN. The highest classification accuracy obtained in the presented work is 99% for healthy subject data versus epileptic data is obtained by SVM. However, the corresponding accuracy between normal subject data and epileptic data using SVM is obtained as 99% and 93% for the eyes open and eyes shut conditions, respectively. The detailed analysis of the methodology and results has been discussed in the paper.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

scholarly journals An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Kui Feng ◽  
Jing Jin ◽  
Ian Daly ◽  
Jiale Zhou ◽  
Yugang Niu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Channel Selection ◽  
Selection Method ◽  
Selection Methods ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Novel Method ◽  
Electroencephalogram Eeg

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

scholarly journals Mental Workload Classification Method Based on EEG Independent Component Features

2020 ◽  
Vol 10 (9) ◽  
pp. 3036 ◽  
Author(s):  
Hongquan Qu ◽  
Yiping Shan ◽  
Yuzhe Liu ◽  
Liping Pang ◽  
Zhanli Fan ◽  
...  
Keyword(s):  
Human Resources ◽  
Classification Accuracy ◽  
Mental Workload ◽  
Independent Component ◽  
Classification Method ◽  
Speech Signals ◽  
Eeg Signals ◽  
Independent Components ◽  
Brain Signals ◽  
Electroencephalogram Eeg

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

scholarly journals Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yuanfa Wang ◽  
Zunchao Li ◽  
Lichen Feng ◽  
Chuang Zheng ◽  
Wenhao Zhang
Keyword(s):  
Computational Complexity ◽  
Extreme Learning Machine ◽  
Classification System ◽  
Classification Accuracy ◽  
Detection System ◽  
Automatic Detection ◽  
Seizure Detection ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Learning Machine

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

scholarly journals Special Issue on Brain Machine/Computer Interface and its Application

2020 ◽  
Vol 32 (4) ◽  
pp. 723-723
Author(s):  
Shoichiro Fujisawa ◽  
Minoru Fukumi ◽  
Jianting Cao ◽  
Yasue Mitsukura ◽  
Shin-ichi Ito
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Eeg Analysis ◽  
Special Issue ◽  
Eeg Signals ◽  
Auditory P300 ◽  
Environment Control ◽  
Electroencephalogram Eeg ◽  
Human Support

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

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