Classification of EEG for Epilepsy Diagnosis in Wavelet Domain Using Artifical Neural Network and Multi Linear Regression

2006 ◽  
Author(s):  
E. Ercelebi ◽  
A. Subasi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Wavelet Domain ◽  
Artifical Neural Network ◽  
Epilepsy Diagnosis ◽  
Multi Linear Regression

A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

2006 ◽  
Vol 45 (06) ◽  
pp. 610-621 ◽  
Author(s):  
A. T. Tzallas ◽  
P. S. Karvelis ◽  
C. D. Katsis ◽  
S. Giannopoulos ◽  
S. Konitsiotis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Epileptic Activity ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Epilepsy Diagnosis ◽  
Transient Events ◽  
Artificial Neural ◽  
Eeg Recordings

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-18
Author(s):  
Qi Xin ◽  
Shaohao Hu ◽  
Shuaiqi Liu ◽  
Ling Zhao ◽  
Shuihua Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Traumatic Injury ◽  
Recurrence Plot ◽  
Superior Performance ◽  
Eeg Classification ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

Statistical Downscaling of Temperature Using Global Climate Model Outputs - Effect of Bias correction

2021 ◽  
Author(s):  
Poonam Wagh ◽  
Roshan Srivastav
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
General Circulation ◽  
Climate Model ◽  
Global Climate Model ◽  
Systematic Bias ◽  
Multi Linear Regression ◽  
Cascade Neural Network

<p>General Circulation Models (GCMs) are the primary source of knowledge for constructing climate scenarios and provide the basis for quantifying the climate change impacts at multi-scales and from local to global. However, the climate model simulations have a lower resolution than the desired watershed or hydrologic scale. Different downscaling methodologies are adopted to transform the global scale (coarser resolution) climate information to the local scale (finer resolution). One of the drawbacks of the GCM simulations is the systematic bias relative to historical observations. Bias correction is thus required to adjust the simulated values to reflect the observed distribution and statistics.<strong> </strong>In this study, the effect of bias correction is evaluated on the statistical downscaling models' performance to predict the temperature. Three statistical downscaling models are used: (i) Multi-linear Regression (MLR); (ii) Generalized Regression Neural Network (GRNN); and (iii) Cascade Neural Network (CasNN). The average daily temperature simulations generated by 25 GCMs of Coupled Model Intercomparison Project Phase-5 (CMIP5) are used in the study. The analysis is carried out at 22 stations of the Upper Thames River Basin (UTRB) in Canada during the baseline period of 1950 to 2005. The downscaling models' performance is evaluated using the Pearson Correlation Coefficient (CC) and Nash Sutcliffe Efficiency (NSE). The results indicated that bias correction had improved all the downscaling models' performance at all stations of UTRB. The respective increase in CC and NSE values for (i) MLR is 8% and 10%; (ii) GRNN is 4% and 7%; and (iii) CasNN is 4% and 8%. Among the three downscaling models, multi-linear regression and cascade neural network models have shown similar performance.</p>

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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