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Literature DB >> 28719805

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.

Asmat Zahra¹, Nadia Kanwal², Naveed Ur Rehman³, Shoaib Ehsan⁴, Klaus D McDonald-Maier⁵.

Abstract

We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm. MEMD is a multivariate extension of empirical mode decomposition (EMD), which is an established method to perform the decomposition and time-frequency (T-F) analysis of non-stationary data sets. We select suitable feature sets based on the multiscale T-F representation of the EEG data via MEMD for the classification purposes. The classification is achieved using the artificial neural networks. The efficacy of the proposed method is verified on extensive publicly available EEG datasets.

Entities: Disease

Keywords: EEG signals; Epilepsy; MEMD; Time-frequency algorithm

Mesh：

Year: 2017 PMID： 28719805 DOI： 10.1016/j.compbiomed.2017.07.010

Source DB: PubMed Journal: Comput Biol Med ISSN： 0010-4825 Impact factor: 4.589

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Cited

13 in total

1. SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification.

Authors: Sunil Kumar Prabhakar; Seong-Whan Lee
Journal: IEEE Open J Eng Med Biol Date: 2022-03-23

2. A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal.

Authors: Marzieh Savadkoohi; Timothy Oladunni; Lara Thompson
Journal: Biocybern Biomed Eng Date: 2020-07-16 Impact factor: 5.687

3. A Deep Convolutional Neural Network Method to Detect Seizures and Characteristic Frequencies Using Epileptic Electroencephalogram (EEG) Data.

Authors: Md Rashed-Al-Mahfuz; Mohammad Ali Moni; Shahadat Uddin; Salem A Alyami; Matthew A Summers; Valsamma Eapen
Journal: IEEE J Transl Eng Health Med Date: 2021-01-11 Impact factor: 3.316

Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.

1. SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification.

2. A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal.

3. A Deep Convolutional Neural Network Method to Detect Seizures and Characteristic Frequencies Using Epileptic Electroencephalogram (EEG) Data.

4. A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach.

5. Brain wave classification using long short-term memory network based OPTICAL predictor.

6. A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

7. A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks.

8. Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition.

9. Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals.

Review 10. Tool Condition Monitoring for High-Performance Machining Systems-A Review.