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Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD)...
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
Support vector machines (SVM) have become a gold standard method for the classification of brain signals. However, for highly nonlinear and non-stationary signals like Electroencephalography (EEG), conventional SVM is not sufficient to classify the different brain states associated with different cognitive activity. Brain state classification is a challenging task when using standard SVM. Thus, a...
The Complexity-Entropy Causality Plane (CECP) is a representation space with two dimensions: normalized permutation entropy (Hs) and Jensen-Shannon complexity (Cjs). CECP has wide found applications in non-linear dynamic analysis to classify a given signal according to its randomness and complexity which is a motivation to investigate its application for machine fault diagnostics. In this work we...
Epileptic seizure detection requires the study of electroencephalogram (EEG) data. Visual marking of seizure onset in such EEG recordings is quite tedious, naturally subjective, extremely time consuming, and it may lead to inaccurate detection. Thus, the development of a robust framework for automatic seizure classification is necessary and can be very useful in epilepsy investigation. In this paper,...
Based on the recently proposed method for nonlinear and non-stationary vibration signal, variational mode decomposition (VMD), an adaptive multiscale fuzzy entropy (AMFE) method is introduced in this paper. Firstly, the VMD method is used to decompose the vibration signals of rolling bearing into a number of intrinsic mode functions (IMFs). Then the fuzzy entropy of each IMF is computed. Meanwhile,...
EEG contains immense information about the brain activity which cannot be understood completely by visual inspection. Powerful signal processing algorithms in EEG analysis can greatly assist the physicians and neurologists to extract such hidden information. It has been found that EEG being a time-varying and non-stationary signal, can be analyzed by non-linear methods. In this paper we tried to evaluate...
Weighted-permutation entropy (WPE) is modified from permutation entropy (PE), which recently has been proposed as a measurement for nonlinear time series. To explore the efficiency of this method and the features of seizure electroencephalogram (EEG) segments, we investigate the application of WPE in the complexity analysis for epileptic seizure detection based on EEG. It is found that the calculated...
Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex...
Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used...
Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
Sixteen conventional heart beat variability (HRV) parameters and eight vital signs have shown promise in the prediction of cardiac arrest within 72 hours. Besides these 24 parameters, we proposed adding two new features for cardiac arrest prediction, which are approximate entropy (ApEn) and sample entropy (SpEn). ApEn and SpEn are nonlinear HRV parameters capable of characterizing heart conditions...
Many algorithms used for the analysis of physiological signals include hyper-parameters that must be selected by the investigator. The ultimate choice of these parameter values can have a dramatic impact on the performance of the approach. Addressing this issue often requires investigators to manually tune parameters for their particular data-set. In this study, we illustrate the importance of global...
Battery prognostics aims to predict the remaining life of a battery and to perform necessary maintenance service if necessary using the past and current information. A reliable prognostic model should be able to accurately predict the future state of the battery such that the maintenance service could be scheduled in advance. In this paper, a multistep-ahead prediction model based on the mean entropy...
Approximate Entropy (ApEn) and Permutation Entropy (PE) have been recently introduced for assessment of anesthetic depth. Both measures have previously been shown to track changes in the electrical brain activity related to the administration of anesthetic agents. In this paper ApEn and PE are compared for the automatic classification of ‘awake’ and ‘anesthetized’ state using a Support Vector Machine...
This paper investigates the use of Permutation Entropy (PE) as a feature for mental task classification for a Brain-Computer Interface system. PE is a recently introduced measure which quantifies signal complexity by measuring the departure of a time series from a random one. More regular signals are characterized by lower PE values. Here, PE is utilized to characterize signals from electroencephalograms...
Chaotic behaviour has been shown to exist in financial data. This paper advances the use of the sparse kernel machine model for the prediction of directional change for this class of dynamical systems. The notions of low entropy trajectory sets and low entropy trajectory balls in phase space are defined as the building patterns for the predictor. The statistical stability and robustness of the sparse...
Based on the characteristic that the Empirical Mode Decomposition (EMD) can decompose signal adaptively, a flow pattern identification method based on EMD multi-scale information entropy was put forward. Firstly, the acquired pressure-difference fluctuation signals are decomposed through EMD, and the decomposed signals within different frequency bands are obtained adaptively. Secondly, the multi-scale...
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