Epilepsy seizure detection using EEG signals

EEG signal processing involves multiple algorithms in which epileptic data is received in the MATLAB environment and needs to be processed in order to obtain a perfectly filtered waveform and process it in both the time and frequency domain. In our work we have shown the EEG signal in the frequency domain using Fast Fourier Transform and its absolute value. Using Wavelet decomposition technique we divide the EEG signal into different sub-level bands then the lowest frequency sub-band was selected to perform feature extraction. Discrete Wavelet Transform (DWT) was applied and Vector Analysis was used for feature extraction and then we have used Inverse Discrete Fourier Transform to transform from frequency to time domain so that frequency analysis of the feature extracted EEG signal could fetch the best results. We have used the lowest frequency band possible between 1 and 3.45 Hz which could be the smallest possible in order to either classify a signal or to apply threshold and compare the results. In order to verify our work, we are comparing our results with some of the mostly used classifiers results even though classifiers do not show frequency analysis

Detection of Epileptic Seizure Using EEG Sensor

The epileptic seizure is a disease of central nervous system. Its detection by the physical analysis of the person?s body is very difficult. So, the appropriate detection of the seizure is very crucial in diagnosis of the person with seizure. The person with epileptic seizure which affects the brain signal can be detected by analyzing the brain signals using EEG sensor. The electroencephalogram (EEG) signal is very essential in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that needs a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for detecting epileptic seizures using wavelet transform and statistical analysis. The decision making process is comprised of three different stages: (1) filtering of EEG signals given as input (2) feature extraction based on wavelet transform, and (3) classification by SVM classifier. The signal from brain given as an input to EEG sensor is analyzed using MATLAB by signal processing technique

A Comparative Analysis of Feature Extraction Techniques for EEG Signals from Alzheimer patients

This research deals with the study of Alzheimer Disease (AD). Electroencephalogram (EEG) signal is a clinical tool for the diagnosis and detection of AD. EEG signals are analyzed for the diagnosis of AD applying several linear and non-linear methods of signal processing. This work studies and implements several measures of EEG signal complexity and then compares the complexity features measured or extracted from EEG signals. Time domain analysis of EEG signals is performed using several signal processing techniques such as higher order moments, entropies and fractal dimension calculation using fractal analysis. Frequency domain analysis of EEG signals is performed using signal processing techniques such as Welch Power spectrum and Discrete Fourier Transform (DFT). EEG signal analysis using Wavelet Transform was also performed. Higher order moments, entropies, fractal dimension estimation using fractal analysis and Welch Power Spectrum are also implemented along with moving windows. This work also deals with the artifact removal or de-noising of EEG signals using a band pass filter. EEG signal data recorded from AD subjects and their respective age-matched control subjects are used to test the performance of the methods in diagnosing AD. In addition, this work outlines the drawbacks of the methods used and compares the methods for the best feature extraction techniques

Improved time-frequency features and electrode placement for EEG-based biometric person recognition

This work introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The feature extraction method is based on the wavelet transform of the raw EEG signal. The logarithms of wavelet coefficients are further processed using the discrete cosine transform (DCT). The DCT coefficients from each wavelet band are used to form the feature vectors for classification. As an application in the biometrics scenario, the effectiveness of the electrode locations on person recognition is also investigated, and suggestions are made for electrode positioning to improve performance. The effectiveness of the proposed feature was investigated in both identification and verification scenarios. Identification results of 98.24% and 93.28% were obtained using the EEG Motor Movement/Imagery Dataset (MM/I) and the UCI EEG Database Dataset respectively, which compares favorably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario when compared with some similar systems from the published literature. A multi-session analysis is simulated using with eyes open and eyes closed recordings from the MM/I database. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

An Investigation of How Wavelet Transform can Affect the Correlation Performance of Biomedical Signals : The Correlation of EEG and HRV Frequency Bands in the frontal lobe of the brain

© 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedRecently, the correlation between biomedical signals, such as electroencephalograms (EEG) and electrocardiograms (ECG) time series signals, has been analysed using the Pearson Correlation method. Although Wavelet Transformations (WT) have been performed on time series data including EEG and ECG signals, so far the correlation between WT signals has not been analysed. This research shows the correlation between the EEG and HRV, with and without WT signals. Our results suggest electrical activity in the frontal lobe of the brain is best correlated with the HRV.We assume this is because the frontal lobe is related to higher mental functions of the cerebral cortex and responsible for muscle movements of the body. Our results indicate a positive correlation between Delta, Alpha and Beta frequencies of EEG at both low frequency (LF) and high frequency (HF) of HRV. This finding is independent of both participants and brain hemisphere.Final Published versio

Classification of Epileptic EEG Signals by Wavelet based CFC

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio