ArticleMedical & biological engineering & computing2019
Real-time epileptic seizure prediction based on online monitoring of pre-ictal features.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features.Physical and engineering sciences in medicine · 2022Article
- Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures.Frontiers in neurology · 2021Article
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3 authors.
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Abstract
Reliable prediction of epileptic seizures is of prime importance as it can drastically change the quality of life for patients. This study aims to propose a real-time low computational approach for the prediction of epileptic seizures and to present an efficient hardware implementation of this approach for portable prediction systems. Three levels of feature extraction are performed to characterize the pre-ictal activities of the EEG signal. In the first-level, the line length algorithm is applied to the pre-ictal region. The features obtained in the first-level are mathematically integrated to extract the second-level features and then the line lengths of the second-level features are calculated to obtain our third-level feature. The third-level information is compared with predefined threshold levels to make a decision on whether the extracted characteristics are relevant to a seizure occurrence or not. The validity of this algorithm was tested by EEG recordings in the CHB-MIT database (97 seizures, 834.224 h) for 19 epileptic patients. The results showed that the average sensitivity was 90.62%, the specificity was 88.34%, the accuracy was 88.76% with the average false prediction rate as low as 0.0046 h
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