A robust methodology for classification of epileptic seizures in EEG signals

Tzimourta, Katerina D.; Tzallas, Alexandros T.; Γιαννακέας, Νικόλαος; Astrakas, Loukas G.; Tsalikakis, Dimitrios G.; Angelidis, Pantelis; Tsipouras, Markos G.

doi:10.1007/s12553-018-0265-z

Cited by 83 publications

(32 citation statements)

References 37 publications

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“…Methods proposed by Raghu et al [42] yielded excellent performance for both classification tasks on Bonn database. Moreover, previous studies also proposed wavelet-based methods [43,44] and achieved a 100% or almost 100% classification accuracy. Akut et al [45] proposed a wavelet-based deep learning approach in which no manual feature extraction was required.…”

Section: Comparison With Previous Studies Based On Bonn Databasementioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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Section: Comparison With Previous Studies Based On Bonn Databasementioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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“…In general, the frequency range of EEG signals extends from 0 to 100 Hz, which is divided into sub-bands: delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz). Raw EEG signals may contain noise from different sources, such as electric or electromagnetic fields.…”

Section: F Effect Of the Frequency Bandmentioning

confidence: 99%

“…Another recently developed method that also achieved classification accuracy of up to 98.78% was proposed by Li et al [12] using waveletbased envelope analysis for feature extraction and neural network ensemble as a classifier. Tzimourta et al [13] presented a multicenter methodology for automated seizure detection based on DWT. The extracted feature vector was used to train a Random Forest classifier to achieve classification accuracy of 95%.…”

Section: Introductionmentioning

confidence: 99%

Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

et al. 2021

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Electroencephalogram (EEG) signals reflect the activities or electrical disturbances in neurons in the human brain. Therefore, these signals are vital for diagnosing certain brain disorders. This study mainly focused on the diagnosis of epilepsy and autism spectrum disorders (ASDs) through the analysis and processing of EEGs. In this study, artifacts were removed from the EEG datasets using Independent Component Analysis and were filtered using a fifth-order band-pass Butterworth filter to remove interference and noise. Next, new methods were used to extract the features of EEGs using common spatial pattern (CSP). It is known that conventional CSP uses variance. However, here the use of entropy, energy, and band power with CSP was proposed to extract features of EEGs. Then, in our investigation, four techniques were employed for classification, namely, linear discriminant analysis, support vector machine, k-nearest neighbor (KNN), and artificial neural network, with the aim of comparing the proposed methods and recommending the optimal combination for the diagnosis of epilepsy and ASDs. Finally, the effects of segment length, frequency band, and reduction number on the results were investigated. Two EEG datasets were employed to verify the proposed methods: the King Abdulaziz University dataset (for ASD) and the MIT dataset (for epilepsy). The results indicated that the extracted features based on CSP and band LBP produced the best performance and that the combination of CSP-LBP-KNN provided the best performance with average classification accuracy of approximately 98.46% and 98.62% for diagnosing ASDs and epilepsy, respectively.

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“…The study had a sensitivity of 98.09% and a specificity of 98.69%. The study of Tzimourta et al [20] presented a methodology for detecting epileptic seizures based on a discrete wavelet transform (DWT) of five levels and a random forest classifier. Five features using DWT were extracted from each dataset from the University of Bonn dataset and the University Hospital Freiburg database.…”

Section: Related Workmentioning

confidence: 99%

Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

Baykara¹,

Abdulrahman²

2021

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Epilepsy is one of the most common chronic disorder which negatively affects the patients' life. The functionality of the brain can be obtained from brain signals and it is vital to analyze and examine the brain signals in seizure detection processes. In this study, we performed machine learning-based and signal processing methods to detect epileptic signals. To do that, we examined three different EEG signals (healthy, ictal, and interictal) with two different classes (healthy ones and epileptic ones). Our proposed method consists of three stages which are preprocessing, feature extraction, and classification. In the preprocessing phase, EEG signals normalized to scale all samples into [0,1] range. After Stockwell Transform was applied and chaotic features and Parseval's Energy collected from each EEG signal. In the last part, EEG signals were classified with ELM (Extreme Learning Machines) with different parameters. Our study shows the best classification accuracy obtained from the Sigmoid activation function with the number of 100 hidden neurons. The highlights of this study are: Stockwell Transform is used; Entropy values are selected based on the adaptive process. Threshold values are determined according to the error rates; ELM classifier algorithm is applied.

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A robust methodology for classification of epileptic seizures in EEG signals

Cited by 83 publications

References 37 publications

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

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