An efficient seizure prediction method using KNN-based undersampling and linear frequency measures

Ghaderyan, Peyvand; Abbasi, Ataollah; Sedaaghi, Mohammad Hossein

doi:10.1016/j.jneumeth.2014.05.019

Cited by 72 publications

(25 citation statements)

References 25 publications

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“…All earlier studies tried to optimize the classifier performance for 20s windows [4], [24], [25], reflecting an assumption that good classification performance for 20s windows would result in accurate seizure prediction. This approach has two methodological flaws.…”

Section: Discussionmentioning

confidence: 99%

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Shiao

Cherkassky

Lee

et al. 2017

IEEE Trans. Biomed. Eng.

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Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages.

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Section: Discussionmentioning

confidence: 99%

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Shiao

Cherkassky

Lee

et al. 2017

IEEE Trans. Biomed. Eng.

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“…The double cross-validation method allows to have an unbiased estimation of SVM accuracy [ 40 ]. A similar approach was previously applied in the prediction of seizures using EEG recordings [ 5 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis

et al. 2018

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Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.

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“…The time domain and frequency domain are used for signal processing when the EEG is assumed to be a stationary signal. On the other hand, when the EEG signal is considered nonstationary [ 4 , 5 ], then the time-frequency domain is employed. Case studies demonstrated that the time-frequency domain is more suitable for EEG signal analysis and could obtain significant results [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

Sharaf

ElSoud

El-Henawy

2018

International Journal of Biomedical Imaging

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Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.

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An efficient seizure prediction method using KNN-based undersampling and linear frequency measures

Cited by 72 publications

References 25 publications

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

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