Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

Zahra, Asmat; Kanwal, Nadia; Rehman, Naveed ur; Ehsan, Shoaib; McDonald-Maier, Klaus

doi:10.1016/j.compbiomed.2017.07.010

Cited by 115 publications

(57 citation statements)

References 15 publications

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“…is method achieved an accuracy of 100% and 98.6% of maximum efficiency for two-class and three-class classifications, respectively. Zahra et al [12] presented a data-driven approach to classify five-class EEG classification using the multivariate empirical mode decomposition (MEMD) algorithm. And ANN was employed to be a classifier, which achieved 87.2% accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.

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Section: Introductionmentioning

confidence: 99%

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

Zhao

Wang

et al. 2020

Computational and Mathematical Methods in Medicine

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“…The subsequent choice of classification can be mapped with the user interface to uncover data about the physical process, where the signal is created. Types of classification: The EEG signals are classified into supervised [13][14][15][16][17][18][19][20][21][22][23][24][25] and unsupervised classification 12 . Major biomedical researches uses supervised classification to manage large data with information related to the dataset or the obtaining the information of class labels by training the classifier.…”

Section: Eeg Signal Classificationmentioning

confidence: 99%

Review on diverse approaches used for epileptic seizure detection using EEG signals

Baskar

Karthikeyan

2018

Bangladesh J Med Sci

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Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal. This detection by the expert clinicians is considered sensitive to bias and time consuming. Further, it suffers from various problems like unsustainability in larger dataset processing and low power detection. Hence, many computerized detection approaches are highly preferred to eliminate the aforementioned problems and to expedite the research in epilepsy seizure detection for aiding the medical professionals. Many such automated epilepsy diagnosis framework has been designed by various researches, which is made to operate in a single or in a combined manner with other domains. This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures. Further, this study summarizes various methods used previously to analyze the epilepsy and seizures based on its state of art approach. Also, investigations are carried out in terms of performance evaluation to find the best suitable epileptic seizure detection technique in the application of Neuro-informatics.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.526-531

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“…This method can be used to acquire the different scales of the original signal intrinsic characteristics and completely eliminate the linear and stationary constraints. Until now, the HHT has been widely used in biomedicine (Zahra, Kanwal, Ur, Ehsan, & McDonald‐Maier, ), fault diagnosis and localization (Lei, He, & Zi, ; Peng, Tse, & Chu, ), oceanography (Song, Bai, Dong, & Song, ), earthquake engineering (Wang, Zhang, Yu, & Zhang, ), paleoclimatology (Liu et al, ; Qian, Wu, Fu, & Wang, ), image processing (Liu, Li, Gong, et al, ; Nunes & Deléchelle, ), and some other fields. The key process in HHT is the empirical model decomposition (EMD), and this process has a so‐called “model mixing” problem, which is defined as “a single intrinsic mode function (IMF) either consisting of widely disparate scale signals or a signal of a similar scale residing in different IMF components” (Huang & Wu, ).…”

Section: Introductionmentioning

confidence: 99%

Application of the Hilbert–Huang transform for recognition of active gully erosion sites in the Loess Plateau of China

Xue

et al. 2019

Transactions in GIS

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The loess shoulder-lines are known to be the most critical topographic structural lines in the loess landform area of the Loess Plateau, China. Previous studies have shown that gully erosion activity can be reflected in the dynamic changes of the shoulder-lines. In this study, active gully erosion sites along the shoulder-lines are investigated. Three typical test areas with different loess landforms are selected, and the loess shoulder-lines are derived from 1 m resolution digital elevation models. First, height profiles of the loess shoulder-lines are extracted. Then, from the perspective of frequency domains, active gully erosion sites are identified using the Hilbert-Huang transform (HHT). The experimental results show that the HHT method can effectively mine the intrinsic characteristics of the shoulder-lines. The detailed information components (non-components) and trend components of the height profiles can be obtained via ensemble empirical mode decomposition and a significance test. In addition, the HHT method was employed to identify the instantaneous frequency curve catastrophe points of the loess shoulder-line height profiles, and the physical meaning is clear: a higher frequency denotes greater energy and more severe erosive processes, and the catastrophe point locations represent the

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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition

Cited by 115 publications

References 15 publications

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

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

Review on diverse approaches used for epileptic seizure detection using EEG signals

Application of the Hilbert–Huang transform for recognition of active gully erosion sites in the Loess Plateau of China

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