Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients

Puspita, Juni Wijayanti; Gunadharma, Suryani; Indratno, Sapto Wahyu; Soewono, Edy

doi:10.1016/j.bspc.2017.02.016

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The categorical features of the dataset are encoded to transform these features into numerical values and the continuous data in this study were normalized. For ML approaches, the dataset is randomly split into two: a training dataset which trains the model, and a test dataset where we predict the response variable and check whether the predicted outcome is similar to the actual outcomes, and the validation dataset is considered for the parameter estimates to be incorporated in the training models [20].…”

Section: Methodsmentioning

confidence: 99%

Supervised models to predict the Stunting in East Aceh

Darnila

Maryana

Mawardi

et al. 2022

Int. J. Eng. Scie. and Inform. Technology.

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Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case of child death. Thus, this paper aimed to has excellent method to handle undernutrition case by exploring the efficacy of machine learning (ML) approaches to predict Stunting in East Aceh administrative zones of Indonesia and to identify the most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from East Aceh, a national-representative data is collected from government by using 2019 about stunting data. We explored Random forest commonly used ML algorithms. Random Forest (RF) as an extension of bagging that in addition for taking random sample of data and also uses random subset of features which mitigates over fitting. Our results showed that the considered machine learning classification algorithms by random forest can effectively predict the stunting status in East Aceh administrative zones. Persistent stunting status was found in the east part of Aceh. The identification of high-risk zones can provide more useful information and data to decision-makers for trying to reduce child undernutrition.

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Section: Methodsmentioning

confidence: 99%

Supervised models to predict the Stunting in East Aceh

Darnila

Maryana

Mawardi

et al. 2022

Int. J. Eng. Scie. and Inform. Technology.

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“…The eigenvector strategies, for example, minimum-norm and multiple signal classification (MUSIC) are most appropriate to the signals that can be made of few sinusoids covered in noise 21 . Recently, the feature extraction techniques are combined with various classifiers like: adaptive neuro-fuzzy inference system 39 , support vector machine (SVM) 35 , Global modular PCA with SVM 41 , least square support vector machine (LS-SVM) 45 and artificial neural network (ANN) 31,39 , ANN with Fuzzy relations 32 , multilayer perceptron neural network (MLPNN) 42 , recurrent neural network (RNN) 39 , relevance vector machine (RVM), probabilistic neural network (PNN), mixture of experts (MEs), modified mixture of experts (MMEs), k-NN 15,34 , Genetic algorithm 38 , nonlinear sparse extreme learning machine 43 , Wavelet based envelope analysis (EA) with neural network ensemble 20 , random forest classifier 22,16 , Bayesian classifier 23 , fuzzy entropy model 24 , rule based classifier 26 , weighted extreme learning 13 and logistic tree model. The execution of a classifier depends on the qualities of the classified data.…”

Section: Eeg Signal Classification Methodsmentioning

confidence: 99%

“…The subsequent choice of classification can be mapped with the user interface to uncover data about the physical process, where the signal is created. Types of classification: The EEG signals are classified into supervised [13][14][15][16][17][18][19][20][21][22][23][24][25] and unsupervised classification 12 . Major biomedical researches uses supervised classification to manage large data with information related to the dataset or the obtaining the information of class labels by training the classifier.…”

Section: Eeg Signal Classificationmentioning

confidence: 99%

Review on diverse approaches used for epileptic seizure detection using EEG signals

Baskar

Karthikeyan

2018

Bangladesh J Med Sci

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Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal. This detection by the expert clinicians is considered sensitive to bias and time consuming. Further, it suffers from various problems like unsustainability in larger dataset processing and low power detection. Hence, many computerized detection approaches are highly preferred to eliminate the aforementioned problems and to expedite the research in epilepsy seizure detection for aiding the medical professionals. Many such automated epilepsy diagnosis framework has been designed by various researches, which is made to operate in a single or in a combined manner with other domains. This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures. Further, this study summarizes various methods used previously to analyze the epilepsy and seizures based on its state of art approach. Also, investigations are carried out in terms of performance evaluation to find the best suitable epileptic seizure detection technique in the application of Neuro-informatics.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.526-531

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“…The mathematical design of orthogonal operators based on the Walsh transform has also been made to detect the onset of epileptic seizures in intracranial EEG recordings [10]. Puspita et al (2017a) propose two models to classify IEDS as spikes and sharp waves using the Bayesian approach based on the Walsh transformation profiles developed in Adjouadi et al (2004Adjouadi et al ( , 2005 (2017b) use frequency features with the same baseline for the upslope and downslope of the waves to identify spikes, sharp and wicket spikes using Backpropagation Neural Network. But so far, there are no mathematics formulas that were designed to differentiate interictal discharges and variants.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges

Gunadharma

Rizal

Ruslami

et al. 2021

CBMS

Self Cite

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A number of benign EEG patterns are often misinterpreted as interictal epileptiform discharges (IEDs) because of their epileptiform appearances, one of them is wicket spike. Differentiating wicket spike from IEDs may help in preventing epilepsy misdiagnosis. The temporal location of IEDs and wicket spike were chosen from 143 EEG recordings. Amplitude, duration and angles were measured from the wave triangles and were used as the variables. In this study, linear discriminant analysis is used to create the formula to differentiate wicket spike from IEDs consisting spike and sharp waves. We obtained a formula with excellent accuracy. This study emphasizes the need for objective criteria to distinguish wicket spike from IEDs to avoid misreading of the EEG and misdiagnosis of epilepsy.

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Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients

Cited by 8 publications

References 13 publications

Supervised models to predict the Stunting in East Aceh

Supervised models to predict the Stunting in East Aceh

Review on diverse approaches used for epileptic seizure detection using EEG signals

Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges

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