Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas, Alexandros T.; Tsipouras, Markos G.; Tsalikakis, Dimitrios G.; Karvounis, Evaggelos; Astrakas, Loukas G.; Konitsiotis, Spiros; Tzaphlidou, Margaret

doi:10.5772/31597

Cited by 53 publications

(14 citation statements)

References 141 publications

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“…The prevalence of epilepsy has been reported to range from 0.5 to 2% in the general population (Nunes et al, 2011). It is characterized by sudden recurrent and transient disturbances of perception or behavior resulting from the excessive synchronization of cortical neuronal networks due to abnormal bursts of electrical discharge in the brain (Tzallas et al, 2012). One of the most disabling aspects of the disorder is the unpredictability of the seizures.…”

Section: Introductionmentioning

confidence: 99%

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

et al. 2017

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Epilepsy is a neurological disorder that causes changes in the autonomic nervous system. Heart rate variability (HRV) reflects the regulation of cardiac activity and autonomic nervous system tone. The early detection of epileptic seizures could foster the use of new treatment approaches. This study presents a new methodology for the prediction of epileptic seizures using HRV signals. Eigendecomposition of HRV parameter covariance matrices was used to create an input for a support vector machine (SVM)-based classifier. We analyzed clinical data from 12 patients (9 female; 3 male; age 34.5 ± 7.5 years), involving 34 seizures and a total of 55.2 h of interictal electrocardiogram (ECG) recordings. Data from 123.6 h of ECG recordings from healthy subjects were used to test false positive rate per hour (FP/h) in a completely independent data set. Our methodological approach allowed the detection of impending seizures from 5 min to just before the onset of a clinical/electrical seizure with a sensitivity of 94.1%. The FP rate was 0.49 h−1 in the recordings from patients with epilepsy and 0.19 h−1 in the recordings from healthy subjects. Our results suggest that it is feasible to use the dynamics of HRV parameters for the early detection and, potentially, the prediction of epileptic seizures.

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Section: Introductionmentioning

confidence: 99%

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

et al. 2017

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“…Kumar et al (2012) proposes the classification of epileptic signals by relative wave energy and wave entry. Tzallas et al (2012) reviews the history of epileptic brain signal recognition. Khan et al (2012) uses multi-dimensional wavelet transform to detect epileptic signals.…”

Section: Introductionmentioning

confidence: 99%

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

Zhou

2020

Front. Neurosci.

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Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.

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“…The repercussions of delayed or misdiagnosis could lead to permanent neurobiological, cognitive, social and psychological impairments [1,4]. Hence, their need to automate the process of detecting epileptiform patterns in a much more efficient and robust manner have inspired the development of various intelligent models [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Early attempts [7] have used traditional mimetic techniques that relied on the distinctive attributes of amplitude, slope, height, duration and sharpness values provided by an expert neurophysiologist.…”

Section: Introductionmentioning

confidence: 99%

“…Since then, most of the state-of-the-art has adopted a dual scheme, i.e. feature extraction and its classification for the development of automated epileptic seizure detection models [8,9]. Analysing the frequency bands for epileptic seizure detection has been the foundation for many proposed schemes [5,[10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Still, there has been a lot of disparity in the selection of frequency bands in these studies. While, many researchers have opted to set the decomposition levels up to five [9,10] and selected all the frequency bands, some of the researchers have even chosen to decompose the same datasets till 11th decomposition levels [12] and selected all the frequency bands. It has already been established that the preferred choice of decomposition levels should be six for feature extraction [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Selection of optimum frequency bands for detection of epileptiform patterns

Swami

Bhatia

Tripathi

et al. 2019

Healthcare Technology Letters

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The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.

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Automated Epileptic Seizure Detection Methods: A Review Study

Cited by 53 publications

References 141 publications

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

Early Seizure Detection Based on Cardiac Autonomic Regulation Dynamics

Epilepsy EEG Signal Classification Algorithm Based on Improved RBF

Selection of optimum frequency bands for detection of epileptiform patterns

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