Patient-Specific Epileptic Seizure Onset Detection Algorithm Based on Spectral Features and IPSONN Classifier

Nasehi, Saadat; Pourghassem, Hossein

doi:10.1109/csnt.2013.48

Cited by 35 publications

(14 citation statements)

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“…83 Diverse automatic localization attempts have been made using template-based methods, 84 temporal and spatial information, 85,86 wavelet decomposition and a generalized Gaussian model, 87 spectral features, 88 and transient events in interictal EEG recordings. Characteristics of the EEG ictal activity are a rapid low-voltage discharge with a marked increase of signal frequency and a significant variability in waveforms between patients.…”

Section: Facial Expression Analysismentioning

confidence: 99%

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

et al. 2017

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Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

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Section: Facial Expression Analysismentioning

confidence: 99%

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

et al. 2017

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“…Seizure detection method should be able to automatically adapt it properties to tackle this condition. However most of the proposed methods rely on extensive pre-training process for their machine learning algorithm (e.g, using ANN [11,14,16] or SVM [10,12,[21][22]). Huge training data, both for normal EEG and EEG with seizures, are needed to obtain accurate seizure detector.…”

Section: Introductionmentioning

confidence: 99%

“…These methods could be further categorized into: timedomain (e.g, [10,11]), frequency-domain (e.g, using filter bank [12] and sign periodogram transform [13]), time-frequency domain (e.g, wavelet transform [14][15][16][17]), nonlinear methods (e.g, using various entropies [18][19]) or combination of them (e.g, [10,20]). Other methods include using spatial filter (e.g, common spatial filter [21]) and transforming EEG signal into 2D image (e.g, image texture analysis [22]).…”

Section: Introductionmentioning

confidence: 99%

“…Other methods include using spatial filter (e.g, common spatial filter [21]) and transforming EEG signal into 2D image (e.g, image texture analysis [22]). Different artificial intelligent and machine learning techniques are used also for EEG signal classification such as artificial neural network (ANN) [11,14,16], support vector machines (SVM) [10,12,[21][22] and k-nearest neighbor (KNN) [20].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several promising EEG signal processing and feature extraction methods for seizure detection are proposed [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. These methods could be further categorized into: timedomain (e.g, [10,11]), frequency-domain (e.g, using filter bank [12] and sign periodogram transform [13]), time-frequency domain (e.g, wavelet transform [14][15][16][17]), nonlinear methods (e.g, using various entropies [18][19]) or combination of them (e.g, [10,20]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

E EG - Based Epileptic Seizures Detection with Adaptive Learning Capability

Ibrahim¹,

Majzoub²

2017

ijeei

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Epilepsy is considered one of the most common neurological disorders. Epileptic seizures can be a major life disability that might result in loss of consciousness, and/or injury to oneself or others. This research work aims to develop an epileptic seizure detection method using electroencephalography (EEG) signal analysis. We combine discrete wavelet transform (DWT), Shannon entropy, and statistical feature (standard deviation) to extract distinctive features of a given EEG signal. Knearest neighbors (KNN) automatically classifies the EEG signal by comparing the extracted features with the features of the normal and seizure baseline. Adaptive learning is used to continuously update the two baselines based the user feedback, if needed, to improve its performance over the time. Our proposed method achieved an overall 94.5 % sensitivity tested with up to 570 hours of continuous EEG recording from ten patients with total of 55 seizure events taken from CHB-MIT database. With its simplicity and fast processing time, the proposed method is suitable to be implemented in embedded device which has limited processing resource.

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