Epileptic seizure detection
Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection
... Abstract. Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating ...
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Support Vector Machine Kernel Functions Performance Evaluation in Epileptic Seizure Detection from EEG
... Automatic epileptic seizure detection from electroencephalogram is one the most challenging task due to its unknown mechanism and patient specific epileptic ...classify epileptic ...
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Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection
... Despite the fact that the wavelet domain based feature engineering is an ideal method of feature extraction and selection in EEG signal processing, it is also an effective tool for preprocessing the EEG signals which ...
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A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine
... Values of SampEn are calculated for all normal (healthy segments), interictal (seizure free epileptogenic zone segments) and ictal (epileptic seizure segments) EEG signals, and are fed to two ...
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An Efficient Method for Epileptic Seizure Detection in Long Term EEG Recordings
... initial epileptic spike template (ECS, EIS and EPS), the spatial, wavelet and spectral fea- tures were extracted and ranked as N rows of an N × M matrix, then it is reshaped into an N∙M size vector, (since the ...
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Evolutionary coherence on EEG signals for epileptic seizure detection
... of epileptic seizure EEG, simultaneously high resolution in both temporal and frequency domain is required in feature extraction method, as this is the most basic but crucial step in representing raw data ...
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Various epileptic seizure detection techniques using biomedical signals: a review
... detect epileptic seizures using the features like mean, variance, zero-crossing rate, entropy, root means square (RMS), and autocorrelation with template signals extracted from time domain ...problems, ...
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Informed Under Sampling for Enhancing Patient Specific Epileptic Seizure Detection
... a seizure and the other 25 declared it as a non- seizure, the classifier should be more biased to towards the seizure class and thus declaring the unknown sample a ...
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EEG Signal Analysis Using Fuzzy Approximate Analysis towards Epileptic Seizure Detection
... From the manual observation of long time EEG Recording for the detection of the Epileptic Seizure is time consuming, costly and may have judgemental errors. In the proposed work fuzzy approximate ...
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FPGA Based Architecture Implementation for Epileptic Seizure Detection Using One Way ANOVA and Genetic Algorithm
... ANOVA test with reference to features of seizure EEG signal shows that features RMS, ZCR, and PSD are most relevant and useful features to classify EEG signal. Table II shows RMS, ZCR, and PSD has minimum P-value ...
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A Survey On Approaches For Epileptic Seizure Detection And Prediction
... cross validation was also performed. In the methodology proposed by [15], the EEG signal was converted into matrix format, also called as image like representation in order to maintain important information. The CNN ...
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Epileptic seizure detection from EEG signals using logistic model trees
... healthy, seizure-free, and seizure) into several seg- ments based on specific time interval and then select representative samples by using OAT from each and every segment of the entire signal data of that ...
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A computer aided analysis scheme for detecting epileptic seizure from EEG data
... numerous epileptic seizure detection algorithms have developed from several countries throughout the ...detecting epileptic seizures from EEG ...from epileptic EEG ...of ...
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Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states
... of epileptic disorders and epileptic seizure detection [10–12] based on time–frequency decomposition [13] and wavelet-based spare functional linear model ...for detection of ...
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Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring
... the seizure of the patients suffering from ...for epileptic seizure detection, a mathematical model that represents the human body dynamics during the epileptic seizure is ...an ...
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Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals
... in epileptic seizure detection, such as correlation [12], linear prediction error energy [13], fast Fourier transform (FFT) [14], wavelet transform [15–17], empirical mode decompo- sition (EMD) [18, ...
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GRAPH PATTERN MATCHING IN YEAST DATASET
... ‘‘Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks ’’, Journal of Neuroscience Methods 191 (2010) 101–109 ...
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NEW APPROACH IN COLOR DISTORTION REDUCTION IN UNDERWATER CORAL REEF COLOR IMAGE ENHANCEMENT BASED ON ESTIMATION ABSORPTION USING EXPONENTIAL EQUATION
... Zarita, et al. [12] has developed a superior harmony system for feature selection and applied it in the Epileptic seizure detection. In this scenario, a wrapper-based feature selection technique was ...
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Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure
... Abstract Detection of epileptic seizure in electroen- cephalogram (EEG) signals is a challenging task and requires highly skilled ...puter-aided detection helps neurophysiologist in ...
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Prediction of Temporal Lobe Epilepsy Using CWT-Entropy Based Method
... Around the world more than 60 million people are affected by epilepsy, which is the second most common chronic neurological disorder [1, 2]. Epilepsy is associated with seizures, which results from sudden disturbance of ...
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