Probabilistic prediction of Epileptic Seizures using SVM

Abbaszadeh, Behrooz; Haddad, Tahar; Yagoub, M.C.E.

doi:10.1109/embc.2019.8856286

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…2 ). The mechanism driving the post-ictal state is poorly understood but the leading theory is that subcortical nuclei are dormant when the seizure ends, and deep brain structures connected to cortical seizure foci are disrupted in the ictal phase, leading to a window of subcortical deactivation and decreased FC in post-ictal state 40 . Higher bands (low-gamma and high-gamma) showed increased post-ictal FC compared to interictal state with spikes (Fig.…”

Section: Discussionmentioning

confidence: 99%

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Rijal

Corona

Perry

et al. 2023

Sci Rep

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Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes’ nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II–IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47–100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.

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Section: Discussionmentioning

confidence: 99%

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Rijal

Corona

Perry

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…SVM can work on non-linear data by using a kernel approach to the initial features of the data set (Awad & Khanna, 2015). Kernel functions map lower dimensions to higher dimensions (Abbaszadeh et al, 2019). In this study, the RBF kernel concept or Radial Basic Function is used in the classification process to get better accuracy with the formula:…”

Section: Classifiers 231 Support Vector Machinementioning

confidence: 99%

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Muliawan,

Rizal,

Hadiyoso

2023

JAETS

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This study describes the prediction of heart disease using ensemble classifiers with parameter optimization. As input, a public dataset was taken from UCI machine learning repository, which refers to the dataset at UCI Machine learning. The dataset consists of 13 variables that are considered to influence heart disease. Particle swarm optimization (PSO) was used for feature selection and principal component analysis (PCA) for feature extraction to reduce the features' dimensions. The application of parameter optimization on several machine learning methods such as SVM (Radial Basis Function), Deep learning, and Ensemble Classifier (bagging and boosting) to get the highest accuracy comparison. The results of this study using PSO dimensionality reduction in the public dataset of heart disease resulted in the slightest accuracy compared to PCA. In contrast, the highest accuracy was obtained from optimizing Deep Learning parameters with an accuracy of 84.47% and optimization of SVM RBF parameters with an accuracy of 83.56%. The highest accuracy in the ensemble classifier using bagging on SVM of 83.51%, with a difference of 0.5% from SVM without using bagging.

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“…SVM can work on non-linear data by applying a kernel approach to the data set's initial features. Lower dimensions are mapped to higher dimensions by kernel functions [24].…”

Section: A Support Vector Machine (Svm)mentioning

confidence: 99%

Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

Rizal

Hadiyoso

Fauzi

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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One of the respiratory disorders is obstructive sleep apnea (OSA). OSA occurs when a person sleeps. OSA causes breathing to stop momentarily due to obstruction in the airways. In this condition, people with OSA will be deprived of oxygen, sleep awake and short of breath. Diagnosis of OSA by a doctor can be done by confirming the patient's complaints during sleep, sleep patterns, and other symptoms that point to OSA. Another way of diagnosing OSA is a polysomnography (PSG) examination in the laboratory to analyze apnea and hypopnea. However, this examination tends to be high cost and time consuming. An alternative diagnostic tool is an electrocardiogram (ECG) examination referring to changes in the mechanism of ECG-derived respiration (EDR). So digital ECG signal analysis is a potential tool for OSA detection. Therefore, in this study, it is proposed to classify OSA based on ECG signals using wavelets and statistical parameters. Statistical parameters include mean, variance, skewness kurtosis entropy calculated on the signal decomposition results. The validation performance of the proposed method is carried out using a support vector machine, k-nearest neighbor (k-NN), and ensemble classifier. The proposed method produces the highest accuracy of 89.2% using a bagged tree where all features are used as predictors. From this study, it is hoped that ECG signal analysis can be used to complete clinical diagnosis in detecting OSA.

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Probabilistic prediction of Epileptic Seizures using SVM

Cited by 11 publications

References 16 publications

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization

Obstructive Sleep Apnea Detection based on ECG Signal using Statistical Features of Wavelet Subband

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