as the t-wave section in electrocardiogram (ecg) illustrates the repolarisation phase of heart activity, the information which is accumulated in this section is so significant that can explain the proper operation of electrical activities in heart. long qt syndrome (lqt) and t-wave alternans (twa) have imperceptible effects on time and amplitude of t-wave interval. therefore, t-wave shapes of t...

In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...

In this study, an adaptive neural-fuzzy analysis system is proposed for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data. Associated with enhanced active segment selection and wavelet-fractal features, adaptive fuzzy neural network (AFNN) is used for the recognition of left and right MI data. In addition to continuous wavelet transform (CWT) and Student’s two-sa...

Electrocardiogram is used to measure the rate and regularity of heartbeats to detect any irregularity to the heart. An ECG translates the heart electrical activity into wave-line on paper or screen. For the feature extraction and classification task we will be using discrete wavelet transform (DWT) as wavelet transform is a two-dimensional timescale processing method, so it is suitable for the ...

ECG is a method used to measure the rate and regularity of heartbeats to detect any irregularity to the heart. An ECG translates the heart electrical activity into wave-line on paper or screen. In this paper, discrete wavelet transform (DWT) will be used to extract the relevant information from the ECG input data in order to perform the classification task. Previous study suggests DWT-based fea...

the goal of this research is to predict total stock market index of tehran stock exchange, using the compound method of arima and neural network in order for the active participations of finance market as well as macro decision makers to be able to predict trend of the market. first, the series of price index was decomposed by wavelet transform, then the smooth's series  predicted by using...

accurate groundwater level modeling and forecasting contribute to civil projects, land use, citys planning and water resources management. combined wavelet-artificial neural network (wann) model has been widely used in recent years to forecast hydrological and hydrogeological phenomena. this study investigates the sensitivity of the pre-processing to the wavelet type and decomposition level in ...

Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely ...

In this paper we present an automatic algorithm for segmenting the putamen from brain MRI based on wavelets and neural network. We first locate the position of putamen using wavelet features. The fuzzy cmeans algorithm is then combined with edge detection to segment the grey matter pixels belonging to the putamen in the located region. Moment features are extracted from the segmented objects fo...