Denoising Sparse Autoencoder-Based Ictal EEG Classification

Qiu, Yang; Zhou, Weidong; Yu, Nana; Du, Peidong

doi:10.1109/tnsre.2018.2864306

Cited by 75 publications

(20 citation statements)

References 59 publications

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“…Zeynab is inspired by graphs and use features based on weighted visibility graph to detect seizures. We notice that the accuracy by Qiu et al [57] is a little higher than ours. They learn the features from denoising sparse autoencoder networks and feeds them into the classifier.…”

Section: Discussioncontrasting

confidence: 68%

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

et al. 2019

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Automatic seizure detection has been often treated as a classification problem that aims at determining the label of electroencephalogram (EEG) signals by computer science, as the EEG monitoring is a helpful adjunct to the diagnosis of epilepsy. In most existing work, the traditional signal energy of the EEG has been applied for classification, since the energy pattern of epileptic seizures differs from that of non-seizures. Although they are effective, the accuracy either heavily depends on additional information besides energy or is limited by the shortcoming of energy-based features. To address this issue, the proposed approach achieves the classification based on the instantaneous energy of the EEG signals instead. The proposed approach first measures the instantaneous energy related to changes in the EEG signals. Then, energy behavior over time is characterized by instantaneous energy-based features from different aspects. Finally, the classification is carried out on the features to produce output labels. By processing instantaneous energy, the information of energy evolution is involved. As such, the accuracy is improved without bringing in extra information besides energy, or complicated transformation. In multi-class problems, the proposed approach has obtained promising results for identifying the ictal EEG, which indicates the tremendous potential of the proposed approach for epileptic seizure detection. INDEX TERMSSeizure identification, electroencephalogram (EEG) signal, instantaneous energy, energy behavior.

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Section: Discussioncontrasting

confidence: 68%

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

et al. 2019

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“…Features extraction mainly rely on artificial selection, which may lose some significant information of the original data. Some existing researches use sparse representation and dictionary learning to classify and detect epileptic EEG signals and they have achieved good classification results [ 15 , 44 , 45 ]. The classification of epileptic EEG signals based on sparse representation and its variants avoids tedious features extraction, and the algorithm runs fast, which is also worth studying in the future.…”

Section: Discussionmentioning

confidence: 99%

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Liu

2020

Entropy

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Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.

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“…Their architecture in this section consists of three layers, and the final results demonstrated good performance of their approach. Qiu et al [ 115 ] exerted the windowed signal, z-score normalization step of preprocessing EEG signals and imported preprocessed data into the denoising sparse AE (DSpAE) network. In their experiment, they achieved an outstanding performance of 100% accuracy.…”

Section: Epileptic Seizures Detection Based On DL Techniquesmentioning

confidence: 99%

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Shoeibi

Khodatars

Ghassemi

et al. 2021

IJERPH

293

109

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

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Denoising Sparse Autoencoder-Based Ictal EEG Classification

Cited by 75 publications

References 59 publications

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

Identification of Epileptic Seizures by Characterizing Instantaneous Energy Behavior of EEG

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

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