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 Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chandrakar Kamath
Keyword(s):  
Probabilistic Neural Network ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Classification Problems ◽  
Dynamic Features ◽  
Cepstral Coefficients ◽  
Eeg Recordings ◽  
Frequency Changes ◽  
Electroencephalogram Eeg ◽  
The Brain

Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure detection. The comparison is tried on seven different classification problems which encompass all the possible discriminations in the medical field related to epilepsy. In this study, it is found that the overall performance of both the composite vectors deteriorates compared to that of baseline cepstral vector.

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.

Visual Imagery Classification Using Shapelets of EEG Signals

2012 ◽  
Author(s):  
Stewart Contreras ◽  
V. Sundararajan
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Signal To Noise Ratio ◽  
Event Related Potential ◽  
Eeg Signals ◽  
Classification Rate ◽  
Average Classification Accuracy ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.

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.

scholarly journals Detection of human emotions using features based on discrete wavelet transforms of EEG signals

2018 ◽  
Vol 7 (1.9) ◽  
pp. 119 ◽  
Author(s):  
The Jaswini S ◽  
K M Ravikumar
Keyword(s):  
Wavelet Transforms ◽  
Affective Computing ◽  
Prediction Models ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Neural Network Models ◽  
Classification Rate ◽  
Artificial Neural

Affective computing is an emerging area of research in human computer interaction where researchers have developed automated assessment of human emotion states using physiological signals to establish affective human compute interactions. In this paper wepresent an efficient algorithm for emotion recognition using EEG signals for the data acquired by audio- video stimuli. The desired frequency bands are extracted using discrete wavelet transforms. The Statistical features, Hjorth parameters, differential entropy and wavelet features are extracted. Artificial neural networks, Support Vector Machine (SVM) and K- nearest neighbor are used on the extracted feature set to develop prediction models and to classify intofour emotion states likeclam, happy, fear and sad .These Artificial neural network models are evaluated on the acquired dataset and emotions are classified into four different states with over all accuracy of 86.36%.The classification rate of calm, happy, fear and sad states are 90.9%, 63.63%, 90.90 and 100 % respectively.

scholarly journals EEG Signals Analysis for Epileptic Seizure Detection Using DWT Method with SVM and KNN Classifiers

2021 ◽  
pp. 54-62
Author(s):  
Asseel Jabbar Almahdi ◽  
Atyaf Jarullah Yaseen ◽  
Ali Fattah Dakhil
Keyword(s):  
Epileptic Seizure ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Nearest Neighbours ◽  
Long Time ◽  
Implementation Time ◽  
Parameter Values ◽  
Electroencephalogram Eeg ◽  
The Brain

Epilepsy is a critical neurological disorder with critical influences on the way of living of its victims and prominent features such as persistent convulsion periods followed by unconsciousness. Electroencephalogram (EEG) is one of the commonly used devices for seizure recognition and epilepsy detection. Recognition of convulsions using EEG waves takes a relatively long time because it is conducted physically by epileptologists. The EEG signals are analyzed and categorized, after being captured, into two types, which are normal or abnormal (indicating an epileptic seizure).  This study relies on EEG signals which are provided by Arrhythmia Database. Thus, this work is a step beyond the traditional database mission of delivering users’ inquiries; instead, this work is to extract insight and knowledge of such data. The features are extracted from the signals by applying the Discrete Wavelet transform (DWT) method on the input EEG signals. Two different algorithms Support vector machine (SVM) and k-nearest neighbours (KNN) are applied to the extracted features. After using the above method, two different types of EEG are expected by using classification, either to be normal (refers to the normal activeness of the brain) or abnormal (refers to the non-normal activeness of the brain, which may involve epilepsy). The evaluation is based on three parameters (Precision, Recall, and Accuracy), and also on the implementation time. In this research, two different methods are used, the first is the DWT with SVM, and the second is the DWT with KNN. With regard to the three-parameter values and implementation time, it turned out that the second method was more efficient than the first because of its higher accuracy.

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.

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