ArticleFrontiers in physiology2020
Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.BMC medical informatics and decision making · 2025Article
- A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.Scientific reports · 2024Article
- Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives.Royal Society open science · 2024Article
- Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal.Cognitive neurodynamics · 2024Article
- An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy.BMC medical informatics and decision making · 2023Article
- Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.Computational intelligence and neuroscience · 2022Article
- A Study on Seizure Detection of EEG Signals Represented in 2D.Sensors (Basel, Switzerland) · 2021Article
- Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy.PeerJ. Computer science · 2021Article
- Automated epilepsy detection techniques from electroencephalogram signals: a review study.Health information science and systems · 2020Article
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2 authors.
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Abstract
Epilepsy is one of the most common chronic neurological disorders, and therefore, diagnosis and treatment methods are urgently needed for these patients. Many methods and algorithms that can detect seizures in epileptic patients have been proposed. Electroencephalogram (EEG) is one of helpful tools for investigating epilepsy forms in patients, however, an expert in the neurological field must perform a visual inspection to identify a seizure. Such analyses require longer time because of the huge dataset recorded from many electrodes which are put on the human scalp. With the non-stationary nature of EEG, especially during the abnormality periods, entropy measures gain more interest in the field. In this work, by exploring the advantages of both reliable state-of-the-art entropies, fuzzy entropy and distribution entropy, a modified-Distribution entropy (mDistEn) for epilepsy detection is proposed. As the results, the proposed mDistEn method can successfully achieve the same consistency and better accuracy than using the state-of-the-art entropies. The mDistEn corresponds to higher Area Under the Curve (AUC) values compared with the fuzzy entropy and the distribution entropy and yields 92% classification accuracy.
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