scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

scholarly journals Analysis of EEG signal of specific epileptic patient prior to its occurrence

SCITECH Nepal ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 16-23
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Amit Shah ◽  
Bhoj Raj Thapa
Keyword(s):  
Epileptic Seizure ◽  
Predictive Value ◽  
Epileptic Patient ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unique Pattern ◽  
Multi Resolution Analysis ◽  
The Individual

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper is focussed on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi-resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MJT­-BIH of one of the patient with 41 different cases is used. The result showed that a unique pattern is observed during the spectral analysis of the signal over different bands with positive predictive value of 100%, negative predictive value of 82.35% and the overall accuracy of 85.37%. This unique pattern, basically energy burst in two of the bands of the signal can be used as important feature for the early detection of the epileptic seizure. All the results have been simulated within the Matlab environment.

scholarly journals A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Gaowei Xu ◽  
Tianhe Ren ◽  
Yu Chen ◽  
Wenliang Che
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Eeg Signal ◽  
Learning Methods ◽  
One Dimensional ◽  
Eeg Signal Analysis

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 1-24
Author(s):  
Bhagyashri Devi ◽  
M. Mary Synthuja Jain Preetha
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Face Detection ◽  
Orthogonal Transformation ◽  
Principal Component ◽  
Discrete Wavelet ◽  
The Face ◽  
Bird Swarm Algorithm ◽  
Trained Neural Network ◽  
Face Emotion Recognition

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).

scholarly journals Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Muhammad Tariq Sadiq ◽  
Hesam Akbari ◽  
Ateeq Ur Rehman ◽  
Zuhaib Nishtar ◽  
Bilal Masood ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Human Error ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Binary Differential Evolution ◽  
Neural Network Classifiers ◽  
Epilepsy Localization

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

Investigation of Epileptic Seizures and Sleep Disturbance

2021 ◽  
pp. 52-70
Author(s):  
Bharath V. S. ◽  
Miraclin F. ◽  
Bhanu Priyanka ◽  
Bharath K. P. ◽  
Rajesh Kumar M.
Keyword(s):  
Neural Network ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Learning Models ◽  
Hilbert Huang Transform ◽  
Intelligent Devices ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Machine Learning Models

In this chapter, the authors make use of signal processing techniques and machine learning models to analyze the EEG signal. First, the EEG signal is broken down into the frequency sub-bands using a discrete wavelet transform (DWT). Then the kernel principle component analysis (KPCA) method is used to reduce the dimension of data. They input these extracted features into a neural network to find if the patient has an epileptic seizure or not. The results of the classification process due to artificial neural networks (ANN) are studied and analyzed. Also, to recognize the abnormal activities in the EEG signal, caused by changes in neuronal electrochemical activity in epileptic patients, the EEG signal is processed using the Hilbert Huang transform (HHT). Given the wide array of epilepsy, we need to make use of intelligent devices in the treatment of epilepsy by using the patient's neurophysiology for better diagnosis before the clinical operation.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features

2018 ◽  
Vol 28 (07) ◽  
pp. 1850003 ◽  
Author(s):  
Yang Li ◽  
Weigang Cui ◽  
Meilin Luo ◽  
Ke Li ◽  
Lina Wang
Keyword(s):  
Epileptic Seizure ◽  
Classification Accuracy ◽  
Epileptic Seizures ◽  
Gaussian Mixture ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Band Frequency ◽  
Time Frequency ◽  
Frequency Components ◽  
Occurrence Matrix

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

scholarly journals Automated Epileptic Seizures Detection and Classification

2019 ◽  
pp. 555-560 ◽  
Author(s):  
Harshavarthini S ◽  
Aswathy M. P. ◽  
Harshini P ◽  
Priyanka G
Keyword(s):  
Epileptic Seizure ◽  
Probabilistic Neural Network ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Eeg Signals ◽  
Human Errors ◽  
Visual Identification ◽  
Time Period ◽  
Long Time

Detection of epileptic seizure activities from multi-channel electroencephalogram (EEG) signals plays a giant position inside the timely treatment of the sufferers with epilepsy. Visual identification of epileptic seizure in long-time period EEG is bulky and tedious for neurologists, which may additionally cause human errors. An automated device for accurate detection of seizures in a protracted-time period multi-channel EEG is crucial for the scientific prognosis. The features selection is based on discrete wavelet transformation (DWT).and feature extraction based GLCM. In the last stage, Probabilistic Neural Network is employed to classify the Normal and epileptic EEG signals.

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.

Emotion Classification by EEG Signal Generated by Brain using Discrete Wavelet Transform and Artificial Neural Network Backpropagation with Classical Music Stimulus

2019 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Aditya Dimas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classical Music ◽  
Brain Activity ◽  
Piano Music ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Artificial Neural ◽  
Music Stimulus

People feel different emotions when listening to music on certain levels. Such feelings occur because the music stimuli causing reduced or increased brain activity and producing brainwave with specific characteristics. Results of research indicated that classical piano music can influence one’s emotional intelligent. By using Electroenchephalography (EEG) as a brainwave recording instrument, we can assess the effect of stimulation on the emotions generated through brain activity. This study aimed at developing a method that defines the effect of sound to brain activity using an EEG signal that can be used to identify one's emotion based on classical piano music stimulus reaction. Based on its frequency, this signal was the classified using DWT. To train Artificial Neural Network, some features were taken from the signal. This ANN research was carried out using the process of backpropagation

scholarly journals An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed I. Sharaf ◽  
Mohamed Abu El-Soud ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Feature Selection ◽  
Correlation Energy ◽  
Epileptic Seizures ◽  
Compact Set ◽  
Eeg Signal ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Eeg Signals ◽  
Computer Aided ◽  
Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

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