Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Dhiman, Rohtash; Saini, J.S.; Priyanka, .

doi:10.1016/j.asoc.2016.12.009

Cited by 22 publications

(9 citation statements)

References 93 publications

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“…TP denotes true positive, FN denotes false negative, FP denotes false positive, and TN denotes a true negative. Since the most useful information is available in subbands D3-D5 and A5, only they are considered [10]. Features such as Mean Absolute Value, maximum coefficients, minimum coefficients, Standard Deviation, average power, Shannon entropy, and approximate entropy are derived from subbands D3, D4, D5, and A5 for five datasets A, B, C, D, and E.…”

Section: Statistical Parametermentioning

confidence: 99%

See 1 more Smart Citation

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Kavitha

Sharmila

Imoize

et al. 2022

Journal of Healthcare Engineering

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Epileptic patients suffer from an epileptic brain seizure caused by the temporary and unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals are manually studied by medical practitioners as it records the electrical activities from the brain. This technique consumes a lot of time, and the outputs are unreliable. In a bid to address this problem, a new structure for detecting an epileptic seizure is proposed in this study. The EEG signals obtained from the University of Bonn, Germany, and real-time medical records from the Senthil Multispecialty Hospital, India, were used. These signals were disintegrated into six frequency subbands that employed discrete wavelet transform (DWT) and extracted twelve statistical functions. In particular, seven best features were identified and further fed into k-Nearest Neighbor (kNN), naïve Bayes, Support Vector Machine (SVM), and Decision Tree classifiers for two-type and three-type classifications. Six statistical parameters were employed to measure the performance of these classifications. It has been found that different combinations of features and classifiers produce different results. Overall, the study is a first attempt to find the best combination feature set and classifier for 16 different 2-class and 3-class classification challenges of the Bonn and Senthil real-time clinical dataset.

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Section: Statistical Parametermentioning

confidence: 99%

“…ML algorithms have been introduced to ensure this efficiency and accuracy in feature characterization. e digital wavelet transformation (DWT) introduced in [9,10] was able to handle the problem of spikes in epileptic seizures.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Kavitha

Sharmila

Imoize

et al. 2022

Journal of Healthcare Engineering

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“…The epileptic seizure-free EEG signals in datasets C and D were also produced, accordingly. Dataset E describes the epileptic seizure signals, which were collected by placing the electrodes in the epileptogenic zone, as shown in Table 1 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41][42][43]. The sample segment of each dataset is shown in Figure 1.…”

Section: Eeg Data Materialsmentioning

confidence: 99%

“…A confusion matrix generally involves the following parameters: The following aggregate metrics can then be calculated based on the above parameters. The criteria for performance evaluation are usually employed in biomedical studies, which include three parts: sensitivity (the proportion of the total number of labeled ictal EEGs that are correctly classified), specificity (the proportion of the total number of labeled inter-ictal EEGs that are correctly classified), and classification accuracy (the proportion of the total number of EEG signals that are correctly classified) [2,[6][7][8][9][10][13][14][15][16][25][26][27][28][29][30][31][32]34,35,[38][39][40][41]49,53,59].…”

Section: Performance Evaluation-confusion Matrix Metricsmentioning

confidence: 99%

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

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This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

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“…The log-energy entropy quantizes the nonlinear dynamics of EEG signals and defines electrophysiological characters of neurological disease [44]. In this study the entropy at each decomposition level was calculated using [45,46]:…”

Section: Entropymentioning

confidence: 99%

Computational model for detection of abnormal brain connections in children with autism

Askari¹,

Setarehdan²,

Sheikhani³

et al. 2018

J. Integr. Neurosci.

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In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3Dcellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.Many investigators have studied the EEGs and analyzed the signals of autistic subjects [14][15][16]. Sheikhani et al.[15] conducted a study of EEG signals based on Lempel-Ziv frequency methods (LZ) and the short-time Fourier transform (STFT). After evaluating results,

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Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Cited by 22 publications

References 93 publications

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Computational model for detection of abnormal brain connections in children with autism

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