ArticleJournal of biomedical research2020
EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms.
Article in Journal of biomedical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 57 citations in OpenAlex.
- Temporal Lobe Epilepsy Focus Detection Based on the Correlation Between Brain MR Images and EEG Recordings with a Decision Tree.Diagnostics (Basel, Switzerland) · 2024Article
- An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.Scientific reports · 2024Article
- Article
- Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems.Scientific reports · 2023Article
- Identification of TLE Focus from EEG Signals by Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG.Frontiers in neuroscience · 2021Article
- Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction.Journal of biomedical research · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography (EEG) is an oversensitive operation and prone to errors, which has motivated the researchers to develop effective automated seizure detection methods. This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases. The proposed method consists of three steps: (i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis (MSPCA), (ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition (EMD), discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals, and (iii) allocate the feature vector to the relevant class (
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Registered trials
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