Classification results for the EEG epileptic data
Classification of Epileptic & Non Epileptic EEG Signal Using Matlab
... analyse EEG signals as epileptic or non-epileptic for the diagnosis of Epilepsy by using machine learning ...are epileptic in nature are focused on ictal discharge and non-epileptic ...
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Improving classification of epileptic and non-epileptic EEG events by feature selection
... Evangelia Pippa, MSc. She received her BSc degree in Computer Engineering and Informatics and her MSc degree in Computer Science and Engineering from the University of Patras , in 2011 and 2013 respectively. She has ...
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Epileptic Seizure Classification of EEG Image Using SVM
... 𝑁 𝑓𝑜𝑟 𝑢 ≠ 0 (2) The 2D DCT is applied on the image which transforms the image into an image with de correlated image data. The two main properties that describe DCT are De Correlation and Energy Compaction. De ...
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Epileptic Seizure Classification of EEG Image Using ANN
... resistant epileptic patients, the possibility to predict forthcoming seizures could be very useful, not only for the patient‟s safety, but also to have the possibility to stop the unwanted ...
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EEG-Based Classification and Advanced Warning of Epileptic Seizures
... forest classification on EEG data, this research investigates the predictive features among the common EEG frequency bands for one patient with partial complex and partial with secondarily ...
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Fast statistical model-based classification of epileptic EEG signals
... detect epileptic brain activity in real-time from electroencephalography (EEG) ...in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the ...
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Classification of EEG signals for epileptic seizure prediction using ANN
... However, we managed to achieve high accuracies only when MSPCA de-noising method was applied to Freiburg dataset. The accuracy may be further improved by applying dimension reduction or feature selection methods like ICA ...
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Epileptic EEG Signal Classification using Wavelet Time Entropy
... Keywords- EEG, epilepsy, Wavelet Time Entropy, SVM ...using EEG to observe epilepsy. Until now, EEG is the main modality for observing brain wave discharge associated with different types of ...
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Efficient Prediction and Classification of Epileptic Seizures Using EEG Data Based on Univariate Linear Features
... its EEG recordings. This method is not useful as this include viewing of EEG signals for many ...available EEG dataset and it has been observed that average pre-ictal time is 34 ...the EEG ...
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Tensor decompositions and data fusion in epileptic EEG and fMRI data
... as epileptic activity, is often hidden within this noisy ...multidimensional EEG and fMRI, and preserve the structural information de fined by the interde- pendencies among the various modes such as ...
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Classification of Epileptic EEG Using Wavelet Transform & Artificial Neural Network
... term EEG refers that the brain activity emits the signal from head and being ...brain. EEG signal provides valuable information of the brain function and neurobiological disorders as it provides a visual ...
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Ensemble classifier for epileptic seizure detection for imperfect EEG data
... an epileptic seizure from compressed and noisy EEG ...four classification models based on their individual ...the classification accuracy in the presence of noisy and incomplete information ...
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Classification of Normal and Epileptic EEG Signals Using Simple Statistical Feature Extraction
... medical data to be investigated in lesser time with good ...from EEG signals are vital for outstanding epileptic seizure ...testing data, the real test data is 20 normal and 20 ...the ...
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A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures
... artifacts such as head/body movement, chewing, blinking, early stages of sleep, and electrode pops/movements were present in the data. In this work, we have used 66 epochs from 9 different subjects ( Table 1 ). ...
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Classification of epileptic EEG signals based on simple random sampling and sequential feature selection
... five EEG datasets and gained the best classification result with sets A and E when c = 10 and r 2 = 1 for the two methods applied in this ...The results of the proposed method were compared with the ...
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Classification of epileptic EEG signals based on J48 Classifier and Correlation based feature selection
... Epilepsy, EEG, CFS,J48 ...by EEG signal recording, which contain valuable information for understanding ...(ECoG). EEG and ECOG signals have been used to recgnize, analyses and treat a number of ...
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Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy
... ictal EEG, it remains an open question whether they can be detected using short-length EEG ...s EEG segment for classifying interictal and ictal EEG from ...ictal EEG signals with a ...
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Detection Of Epileptic Seizures In EEG Signal
... RVS CET, Coimbatore I. INTRODUCTION Epilepsy is one of the world's most basic neurological infections, influencing more than 40 million individuals around the world. Epilepsy's trademark side effect, seizures, can have ...
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An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure
... Similarly, three kernels such as Linear, Polynomial, and RBF kernels are used in SVM. Hence, this comparative study clearly shows the differences in the efficiency of different machine algorithms with respect to the task ...
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Auto Regressive based Feature Extraction and Classification of Epileptic EEG using Artificial Neural Network
... of EEG recordings for inter-ictal and ictal activities by an experienced ...of EEG data has serious ...acquired EEG signals using auto-regressive features and classify them into di ff erent ...
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