Statistical features based epileptic seizure EEG detection - an efficacy evaluation

K, Gopika Gopan; Sinha, Neelam; J, Dinesh Babu

doi:10.1109/icacci.2015.7275808

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…2. Entropies based detection of an epileptic seizure using EEGs have been studied and well reported in the recent past (Srinivasan et al 2007;Pravin et al 2010;Aydin et al 2009;Wang et al 2011;Gopan et al 2015). Srinivasan et al (2007) have employed ApEn feature with REN network as a classifier and have achieved 100 % CA.…”

Section: Discussionmentioning

confidence: 99%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The nonlinear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictalepileptic.

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Section: Discussionmentioning

confidence: 99%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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“…The two similar works reported by Gopika Gopan et al [43][44] and another reported by Bhuvaneshwari et al [45], used various time domain statistical features such as energy, variance, entropy, median absolute deviation, interquartile range, kurtosis, skewness, and linear prediction coefficient. Khorshidtalab et al [46] also used time domain statistical features for their motor imagery classification problem by analyzing EEG signals.…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

EEG Feature Engineering Methods-A Comprehensive Review

Martin¹

2022

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Today, the primary topic of discussion in the signal processing domain is the analysis of non-stationary and nonlinear signal data. The use of biomedical equipment generates enormous amounts of physiological data that can be analyzed and used for clinical diagnosis. Manual inference of specific decisions from such signals is laborious due to artifacts and the time-series nature of the data, particularly for electroencephalography (EEG) signals. As a result, it is critical to employ appropriate methods for signal analysis. The purpose of this survey is to gain an understanding of the various techniques used to process EEG signal data in epileptic seizure detection frameworks. A variety of classification and regression frameworks based on machine learning have been reviewed. This systematic review will adhere to the PRISMA guidelines. This survey uncovered several significant findings.

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Complex-valued distribution entropy and its application for seizure detection

Zhang

Chen

2020

Biocybernetics and Biomedical Engineering

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Statistical features based epileptic seizure EEG detection - an efficacy evaluation

Abstract: Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities.

Cited by 9 publications

References 30 publications

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

EEG Feature Engineering Methods-A Comprehensive Review

Complex-valued distribution entropy and its application for seizure detection

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