Key result
The Hilbert transform feature extraction combined with the scaled conjugate gradient back propagation algorithm achieved the highest classification accuracy of 87.38% for epileptic EEG signals.
Why the study?
Electroencephalogram signals are prone to artefacts that must be removed, motivating the development of a clinical decision support system using time-frequency and artificial neural network methods to detect epileptic seizures.
Does the combination of Hilbert transform feature extraction and scaled conjugate gradient back propagation improve the classification accuracy of epileptic EEG signals?
Does the combination of Hilbert transform feature extraction and scaled conjugate gradient back propagation improve the classification accuracy of epileptic EEG signals?
The use of Hilbert transform for feature extraction combined with a scaled conjugate gradient back propagation algorithm achieves 87.38% accuracy in classifying epileptic EEG signals, offering a computationally efficient method for real-time seizure detection.
May support automated EEG artefact removal in research settings; leaves open validation for clinical epilepsy monitoring.
Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals. Comprehensive classification results were obtained for the extracted filtered features from the time-frequency domain. The classification accuracies of the time-frequency features obtained from discrete continuous transform (DCT), fractional Fourier transform (FrFT), and Hilbert transform (HT) are compared. Artificial neural networks (ANN) were applied, and back propagation (BP) was used as a learning method. Many studies in the literature describe a single BP algorithm. In contrast, we looked at several BP algorithms including gradient descent with momentum (GDM), scaled conjugate gradient (SCG), and gradient descent with adaptive learning rate (GDA). The most successful algorithm was tested using simulations made on three separate datasets (DCT_EEG, FrFT_EEG, and HT_EEG) that make up the input data. The HT algorithm was the most successful EEG feature extractor in terms of classification accuracy rates in each EEG dataset and had the highest referred accuracy rates of the algorithms. As a result, HT_EEG gives the highest accuracy for all algorithms, and the highest accuracy of 87.38% was produced by the SCG algorithm.
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Bayrak et al. (2021) studied Epilepsy (n=10). Hilbert Transform (HT) feature extraction with Scaled Conjugate Gradient (SCG) back propagation algorithm vs. Discrete Continuous Transform (DCT) and Fractional Fourier Transform (FrFT) with GDM, SCG, and GDA algorithms was evaluated on Classification accuracy. The Hilbert transform feature extraction combined with the scaled conjugate gradient back propagation algorithm achieved the highest classification accuracy of 87.38% for epileptic EEG signals.
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