“…[ 24 ] NA 97.56% 94.93% 98.17% Abiyev et al. [ 2 ] 16-layer 98.67% 98.83% 97.67% Akyol [ 25 ] Stacking Ensemble DNN 97.17% 97.17% 93.11% Our winner one 11-layer 99.43% 99.57% 99.10% Figure 2 Comparison of identification accuracies of all candidate and benchmark architectures. …”
Section: Resultsmentioning
confidence: 99%
“…This successfully achieved an accuracy of 97.56%, a sensitivity of 98.17%, and a specificity of 94.93%. Later, the effectiveness of the stacking ensemble DNN approach for epileptic seizure detection was studied by Akyol [ 25 ]. An average accuracy value of 97.17% was attained, along with an average sensitivity of 93.11%.…”
An electroencephalogram (EEG) measures and records the electrical activity of the brain. It provides valuable information that can be used to identify epileptic abnormalities. However, the visual identification of such abnormalities from EEG signals by expert neurologists is time consuming. Therefore, several researchers have proposed using deep neural networks (DNNs) to automate the identification of these abnormalities. Their studies have examined the use of different numbers of layers, different numbers of parameters, and various operation types arranged in different architectures. This paper presents the shallowest 11-layer DNN architecture capable of classifying three classes of EEG signals: normal, preictal, and seizure. When the proposed architecture was applied to the standard University of Bonn EEG signal dataset, it achieved accuracy, specificity, and sensitivity values of 99.43%, 99.57%, and 99.10%, respectively. It not only had a better performance than the state of the art DNN architectures, but also had shallower layers with fewer parameters. This allowed it to more quickly identify epileptic abnormalities. Experiments were also conducted where the length of the EEG signals was reduced to 65% (2,662 samples with a period of 15.26 s), which in turn minimised the total parameters of the proposed architecture so that it was comparable to the smallest state-of-the-art architecture and decreased the lag time for identification. Even in these experiments, it was capable of producing equal performance measures, with the execution time reduced to only 69% of that when employing the full length of EEG signals.
“…[ 24 ] NA 97.56% 94.93% 98.17% Abiyev et al. [ 2 ] 16-layer 98.67% 98.83% 97.67% Akyol [ 25 ] Stacking Ensemble DNN 97.17% 97.17% 93.11% Our winner one 11-layer 99.43% 99.57% 99.10% Figure 2 Comparison of identification accuracies of all candidate and benchmark architectures. …”
Section: Resultsmentioning
confidence: 99%
“…This successfully achieved an accuracy of 97.56%, a sensitivity of 98.17%, and a specificity of 94.93%. Later, the effectiveness of the stacking ensemble DNN approach for epileptic seizure detection was studied by Akyol [ 25 ]. An average accuracy value of 97.17% was attained, along with an average sensitivity of 93.11%.…”
An electroencephalogram (EEG) measures and records the electrical activity of the brain. It provides valuable information that can be used to identify epileptic abnormalities. However, the visual identification of such abnormalities from EEG signals by expert neurologists is time consuming. Therefore, several researchers have proposed using deep neural networks (DNNs) to automate the identification of these abnormalities. Their studies have examined the use of different numbers of layers, different numbers of parameters, and various operation types arranged in different architectures. This paper presents the shallowest 11-layer DNN architecture capable of classifying three classes of EEG signals: normal, preictal, and seizure. When the proposed architecture was applied to the standard University of Bonn EEG signal dataset, it achieved accuracy, specificity, and sensitivity values of 99.43%, 99.57%, and 99.10%, respectively. It not only had a better performance than the state of the art DNN architectures, but also had shallower layers with fewer parameters. This allowed it to more quickly identify epileptic abnormalities. Experiments were also conducted where the length of the EEG signals was reduced to 65% (2,662 samples with a period of 15.26 s), which in turn minimised the total parameters of the proposed architecture so that it was comparable to the smallest state-of-the-art architecture and decreased the lag time for identification. Even in these experiments, it was capable of producing equal performance measures, with the execution time reduced to only 69% of that when employing the full length of EEG signals.
“…With the continuous development of machine learning, new algorithms are constantly being introduced into seizure detection. Akyol (2020) proposed a new deep neural network for seizure detection that successfully obtained an average accuracy of 97.17%. Choubey and Pandey (2020) used Artificial Neural Network (ANN) and KNN to achieve seizure detection.…”
Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.
“…The study achieved more than 99.17% performance accuracy for the Bonn data set and 99.620% for the CHB-MIT data set. The study of Akyol [16] proposed a model based on a stacking ensemble approach to detect epileptic seizures, in addition to the DNN model. The Bonn data set was used to measure the efficiency of the proposed model and the deep neural networks model.…”
Section: Related Workmentioning
confidence: 99%
“…This theory can be calculated with Eq. ( 16) [34]: (16) (f) represents the Fourier Transform of a signal. In this study, different methods were used from the methods used in the literature, and the methods that were used in this study proved that they can be used with EEG signals that have the advantage of being unstable signals.…”
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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