Time-domain exponential energy for epileptic EEG signal classification

Fasil, O. K.; Rajesh, R

doi:10.1016/j.neulet.2018.10.062

Cited by 78 publications

(8 citation statements)

References 35 publications

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“…Our paper proposes the use of a DNN based on CNNs for epileptic EEG classification and evaluates several signal transformations to generate the inputs to the DNN. The best previous results on the Bern-Barcelona EEG dataset were obtained by Sriraam and Raghu [18] while the best previous results on the Epileptic Seizure Detection dataset were presented by Fasil and Rajesh [17] and Wang et al [45], using SVMs in all cases. These previous works are our baseline in this paper.…”

Section: Related Work On Epileptic Eeg Signal Classificationmentioning

confidence: 92%

“…The plots relating energy and correlation calculations on the IMFs were used as a feature for the classification. Fasil and Rajesh [17] analyzed different time-domain features including several types of entropy like Shannon, Renyi or Tsalli and energy based features, proposing exponential energy as a new feature. They obtained an accuracy of 89.0% on the Bern-Barcelona EEG dataset.…”

Section: Related Work On Epileptic Eeg Signal Classificationmentioning

confidence: 99%

“…After the feature extraction, a classification module must discriminate between the different classes. Support Vector Machines (SVM) have been used extensively for the classification of focal and non-focal EEG signals [17] [18]. Xie et al [29] used a k-Nearest Neighbors (kNN) with k=1.…”

Section: Related Work On Epileptic Eeg Signal Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

San-Segundo¹,

Gil-Martín²,

D’Haro³

et al. 2019

Computers in Biology and Medicine

133

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Section: Related Work On Epileptic Eeg Signal Classificationmentioning

confidence: 92%

Section: Related Work On Epileptic Eeg Signal Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

San-Segundo¹,

Gil-Martín²,

D’Haro³

et al. 2019

Computers in Biology and Medicine

133

View full text Add to dashboard Cite

“…The information of the EEG signals is in the form of frequency domain and wavelet form [6]. The timely treatment of epilepsy aids in the effective diagnosis of the condition, which is usually done through the interpretation of EEG signals by a radiologist or doctor [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Epileptic EEG Signals Using Improved Atomic Search Optimization Algorithm

2023

IJIES

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Epilepsy is a neurological disorder that is distinguished by the presence of seizures, which are caused by abnormal brain activity. On average, about 60% of epilepsy cases are due to focal seizures, which affect a specified part of the cerebrum. Epileptic seizures can be cured at the initial stage, but untreated seizure for a long time cause severe effect and sometimes leads to death. Therefore, accurate classification of the epileptic Electroencephalogram (EEG) signal is an essential step in the diagnosis of epilepsy, aiding physicians in making the diagnosis. This research presents an effective classification of epileptic EEG signals using the improved atomic search optimization (IASO) algorithm and the random search strategy (RSS). The IASO is used to select the appropriate features but has a high probability to enhance the convergence rate. However, due to a lack of population variety and exploratory capabilities and this convergence rate may decrease in some circumstances. As a result, the RSS is offered to enhance the solution and improvise the exploration capabilities. The raw data of EEG signals are obtained from TUH, BONN, and BERN datasets. The raw data is pre-processed using an adaptive filtering method. The IASO is utilized in selecting the relevant features which ease the process of classification using LSTM. The results show that IASO attained better classification accuracy of 98.40% for TUH dataset which is comparatively lower than the existing Deep network model with 97%.

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“…However, there are many signals with certain probability features which cannot be described by a single specific function, such as the spectrum, chromatographic wave, electroencephalogram, seismic wave and so on. If there is a single mathematical description which could depict a set of signals with certain probability characteristics, it would solve many problems in the field of signal processing [8,25].…”

Section: Introductionmentioning

confidence: 99%

A Stopping Criterion for the Training Process of the Specific Signal Generator

Cui

Zhao

et al. 2021

ITC

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Mathematical description of a complex signal is very important in engineering but nearly impossible in many occasions. The emergence of the Generative Adversarial Network (GAN) shows the possibility to train a single neural network to be a Specific Signal Generator (SSG), which is only controlled by a random vector with several elements. However, there is no explicit criterion for the GAN training process to stop, and in real applications the training always stops after a certain big iteration. In this paper, a serious issue was discussed during the process to use GAN as a SSG. And, an explicit criterion for the GAN as a SSG to stop the training process were proposed. Several experiments were carried out to illustrate the issues mentioned above and the effectiveness of the stopping criterion proposed in this paper.

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Time-domain exponential energy for epileptic EEG signal classification

Cited by 78 publications

References 35 publications

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

Classification of epileptic EEG recordings using signal transforms and convolutional neural networks

Classification of Epileptic EEG Signals Using Improved Atomic Search Optimization Algorithm

A Stopping Criterion for the Training Process of the Specific Signal Generator

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