Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant” irregularities while keeping “meaningful” singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for K-means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates.
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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