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Effect of Data Preprocessing in the Detection of Epilepsy using Machine Learning Techniques

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Title Effect of Data Preprocessing in the Detection of Epilepsy using Machine Learning Techniques
 
Creator Sabarivani, A
Ramadevi, R
Pandian, R
Krishnamoorthy, N R
 
Subject Classifier
Convolutional neural network
Normalization
Precision
Regression
 
Description 1066-1077
Epilepsy is the one of the most neurological disorder in our day to day life. It affects more than seventy million people
throughout the world and becomes second neurological diseases after migraine. Manual inspection of seizures is time
consuming and laborious task. Nowadays automated techniques are evolved for detection of seizures by means of signal
processing or through machine learning techniques. In this article, supervised learning algorithms are applied to the EEG dataset
and performance are measured in terms of Accuracy, precision and few more. Machine learning algorithm plays a vital role in
classification and regression problem in the past few decades. The most important reason for this is a large set of signal or data
are trained and the test signals are evaluated using training network. To get the better accuracy, the input data are first
normalized carefully. The various normalization techniques applied in this article are Z-Score, Min-Max, Logarithmic and
Square Root Normalization. For simulation purpose, Electroencephalography (EEG) signal from UCI Machine Learning
Respiratory are used. Dataset consists of 11500 patient details with 5 different cases and each signal are recorded for the
duration of 23 seconds. Spider chart is used to show the metric value in detail. It is observed from the result that supervised
learning algorithm yields a better result compared to logistic and KNN (K-Nearest Neighbor) algorithm at high iteration.
 
Date 2021-12-27T10:37:57Z
2021-12-27T10:37:57Z
2021-12
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58739
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(12) [December 2021]