Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song, Yuedong; Crowcroft, Jon; Zhang, Jiaxiang

doi:10.1016/j.jneumeth.2012.07.003

Cited by 220 publications

(88 citation statements)

References 45 publications

(48 reference statements)

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“…Having a simple training set makes the classifier's training phase faster. Also, most studies [11,12,15,[18][19][20] use a small data set [21] having a total of only 39 minutes seizure data. We tested our method on an EEG dataset of 22 patients (5 males and 15 females ages 1-22) collected at the Children's Hospital Boston (available as CHB-MIT database) [1,2].…”

Section: Proposed Methodsmentioning

confidence: 99%

A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures

Bugeja

Garg

Audu

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Section: Proposed Methodsmentioning

confidence: 99%

A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures

Bugeja

Garg

Audu

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…The EEG dataset was recorded from pediatric subjects with intractable seizures at Children's Hospital Boston. This database contains 22 subjects (17 females, ages 1.5-19; 5 males, ages [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and can be downloaded from the PhysioNet website: http://www .physionet.org/pn6/chbmit/. The International 10-20 system of EEG electrode positions and nomenclature was used to collect these EEG recordings.…”

Section: Eeg Datasetsmentioning

confidence: 99%

“…A small value of SE also indicates more self-similarity and regularity in the dataset. In general, = 0.2 * std ( ) is most common used and offers very good performances [9,11]. To keep consistency with PE, = 3 and = 1 are set for SE.…”

Section: Sample Entropymentioning

confidence: 99%

“…Approximate entropy (ApEn) was first used to study normal and epileptic EEG data in [8]. A modified algorithm of ApEn, called sample entropy (SampEn), was then introduced for automatic epileptic seizure detection in EEG recordings [9]. Generally, ApEn is inherently biased because of incorrectly counting self-matches, while the calculation of SampEn overcomes this limitation, resulting in better performance in detecting seizures [10].…”

Section: Introductionmentioning

confidence: 99%

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Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Zhu

Zhao

et al. 2017

Complexity

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Epilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.

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“…In the EEG signal analysis, information entropies, such as fuzzy entropy, sample entropy, approximate entropy, wave entropy, power spectrum entropy and sort entropy, are often used as entropy-based feature extraction method [10][11][12][13]. These entropies are often used for quantification in the cognitive analysis of EEG signals in different mental state and sleep state, indicating that entropy index is a rather useful tool for EEG analysis.…”

Section: Introductionmentioning

confidence: 99%

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

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Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

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Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Cited by 220 publications

References 45 publications

A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures

A novel method of EEG data acquisition, feature extraction and feature space creation for early detection of epileptic seizures

Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

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