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

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

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wetenschappelijk artikel

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наукова стаття, опублікована у 2018

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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An Automated Approach for Epil ...... ly Feature Selection Algorithm

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P1476

An Automated Approach for Epil ...... ly Feature Selection Algorithm

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P2093

Ibrahim M El-Henawy

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Mohamed Abu El-Soud

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P2860

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

Epileptic EEG classification based on extreme learning machine and nonlinear features.

Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.

Epileptic seizure detection based on imbalanced classification and wavelet packet transform.

Cross-validation failure: Small sample sizes lead to large error bars.

Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating.

Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.

Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state.

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions.

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5812872

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scholarly article

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10.1155/2018/5812872

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P407

English

P478

2018

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P50

Ahmed I Sharaf

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2018-01-01T00:00:00Z

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P698

30275820

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P921

automation

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6151675

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