2013
DOI: 10.11591/ijphs.v2i1.1836
|View full text |Cite
|
Sign up to set email alerts
|

EEG signal classification for Epilepsy Seizure Detection using Improved Approximate Entropy

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
22
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
4
2
2

Relationship

0
8

Authors

Journals

citations
Cited by 33 publications
(22 citation statements)
references
References 17 publications
0
22
0
Order By: Relevance
“…The results are satisfactory: using the FuzzyEn and the SampEn as features, the average accuracies of the CHB-MIT are 98.31% and 97.16%, respectively. Akareddy et al (2013) used the same data and used ApEn as a feature; his accuracy was only 90%, a result that is not better than ours. In this author's study, the specific indexes, such as accuracy, sensitivity and specificity, were not calculated.…”
Section: Discussionmentioning
confidence: 60%
See 1 more Smart Citation
“…The results are satisfactory: using the FuzzyEn and the SampEn as features, the average accuracies of the CHB-MIT are 98.31% and 97.16%, respectively. Akareddy et al (2013) used the same data and used ApEn as a feature; his accuracy was only 90%, a result that is not better than ours. In this author's study, the specific indexes, such as accuracy, sensitivity and specificity, were not calculated.…”
Section: Discussionmentioning
confidence: 60%
“…On the basis of PE, Nicolaou and Georgiou (2012) were the first to perform an epilepsy classification using support vector machines (SVM), and their obtained classification accuracy was 94.38%. Using ANN, Akareddy et al (2013) studied the EEG signals of epileptics based on ApEn, with a classification accuracy of 90%. With the calculated SampEn adopted as the index, Shen et al (2013) also conducted classifications of epilepsy, and their calculated accuracy was as high as 91.18%.…”
Section: Introductionmentioning
confidence: 99%
“…Since EEG signals are transient, and highly dynamic, feature vectors are formed for each time epoch. The Multi-level Wavelet Decomposition is a popular technique used in previous studies [3,10,11,12,13,[15][16][17][18] for feature vector extraction. This technique decomposes an EEG signal into a number of sub-band signals each depicting a different waveform morphology within a particular frequency range.…”
Section: A Feature Vector Designmentioning
confidence: 99%
“…The idea for LDA was proposed by [5] and developed by [6]. Here, the method is to fmd a weight vector wso that NI and N2 ,which are the training feature vectors described bythe sets {XI, X2 , ... , XNI } and {XI, X2 , ... , XN2} respectively can be projected on w.They should be separated by linear hyper planes so that very small variance inside the clusters CI, C2, is obtained.…”
Section: Classificationmentioning
confidence: 99%