Focal and Non-Focal EEG SignalClassification by Computing Areaof 2D-PSR Obtained for IMF

Krishnaprasanna, R.; Baskar, V. Vijaya

doi:10.13052/jicts2245-800x.523

Cited by 5 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence the PSR of IMFs is also elliptical. 2D PSR is a plot between the two vectors Vk & Vk+1 [22 ]. The 2D PSR plot of the first four IMFs computed for the 95% confidence area has been shown in Fig.…”

Section: Feature Extraction For Eeg Signals 231 Area Computation Of Elliptical Pattern For 2d Psrmentioning

confidence: 99%

Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram

Parveen¹

2021

TURCOMAT

View full text Add to dashboard Cite

In this paper, the authors propose a new technique for the classification of seizures, non-seizures, and seizure-free EEG signals based on non-linear trajectories of EEG signals. The EEG signals are decomposed using the EMD technique to obtain intrinsic mode functions (IMFs). The phase space of these IMFs is then reconstructed using a novel technique of higher-order dimensions (3D, 4D, 5D, 6D, 7D, 8D, 9D, and 10D). The existing techniques of seizure detection have deployed 2D & 3D phase–space reconstruction only. The Euclidean distance of all higher-order PSR is used as a feature to classify seizures, non-seizures, and seizure-free EEG signals. The performance of the proposed method is analyzed on the Bonn University database in which 7D reconstructed phase space classification accuracy of 99.9% has been achieved both using Random Forest classifier and J48 decision tree.

show abstract

Section: Feature Extraction For Eeg Signals 231 Area Computation Of Elliptical Pattern For 2d Psrmentioning

confidence: 99%

Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram

Parveen¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…These three have been applied to many cases such as epilepsy detection using KNN [11] to classify epilepsy and nonepilepsy with 87% accuracy. In addition, using ANN [12] with 97.55% accuracy and using the EEG-focal and non-focal classification approach using SVM [13] with an accuracy of about 96.8%. The three algorithms have been used for a long time, and they have a disadvantage in that they cannot be used in large-scale data [14].…”

Section: Introductionmentioning

confidence: 99%

“…Jurnal Penelitian Fisika dan Aplikasinya (JPFA), 2023; 13(1):[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

unclassified

Early Study in Automatic Identification of Epilepsy in Neonatal Using EEGLAB and One Dimensional Convolutional Neural Network Through the EEG Signal

Nadyah,

Syaja'ah,

Waryono Sunaryo

2023

J. Penelit. Fis. Apl.

View full text Add to dashboard Cite

In detecting epileptic activity, medical experts examine the visual result of Electroencephalography signals. The visual analysis will take a lot of time and effort, due to a large amount of data. Furthermore, there are some errors in concluding the analysis result. One of the ways to analyze this quickly is to use Machine Learning (ML) methods. This study aims to evaluate the performance of 1D-CNN in identifying the given data. First, the signal will go through pre-processing using EEGLAB Toolbox which is then classified to identify epilepsy and non-epilepsy with the 1D-CNN algorithm. The results showed that the proposed method obtained high accuracy values, respectively 99,078% for the training data and 82,069% for the validation results. From the evaluation by a confusion matrix, an average accuracy of 99,31% was obtained. Based on this evaluation, the proposed model can be used as an efficient method in the process of automatic classification, detection, or identification of epileptic activity.

show abstract

A Detailed Survey on Epilepsy Detection Using the Classifications of EEG Signals

Bee

Vidhya

2021

Emerging Technologies in Data Mining and Information Security

View full text Add to dashboard Cite

Focal and Non-Focal EEG SignalClassification by Computing Areaof 2D-PSR Obtained for IMF

Cited by 5 publications

References 2 publications

Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram

Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram

Early Study in Automatic Identification of Epilepsy in Neonatal Using EEGLAB and One Dimensional Convolutional Neural Network Through the EEG Signal

A Detailed Survey on Epilepsy Detection Using the Classifications of EEG Signals

Contact Info

Product

Resources

About