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Postdisaster search and rescue is an important application of ultra‐wideband radar system, which mainly detects survivors by their respiratory‐motion response. In the application of detecting multiple human targets, owing to faint amplitude of human chest movement and high attenuation of electromagnetic wave, the motionless living human targets cannot be discriminated accurately, especially in a complex rescue condition with dynamic clutter interference. In this article, on account of the significant difference of frequency components between the echo signal of tested target and that of illusive target produced by dynamic clutter, an algorithm based on wavelet entropy is proposed. Compared with the reference algorithms of adaptive line enhancement and energy accumulation, the wavelet entropy can accurately detect multiple human targets under the condition of dynamic clutter interference, even though the human targets stand far away locate in the shadowing region caused by the closer one. [ABSTRACT FROM AUTHOR]

Purpose: To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy.Materials and Methods: The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated. Using a model time series consisting of multiple harmonics and nonstationary components, the wavelet entropy was compared with Shannon and spectral (Fourier-based) entropy. As an application, the wavelet entropy in 22 children with Rolandic epilepsy was compared to 22 age-matched healthy controls. The images were obtained by performing resting-state functional magnetic resonance imaging (fMRI) using a 3T system, an 8-element receive-only head coil, and an echo planar imaging pulse sequence ( T2*-weighted). The wavelet entropy was also compared to spectral entropy, regional homogeneity, and Shannon entropy.Results: Wavelet entropy was found to identify the nonstationary components of the model time series. In Rolandic epilepsy patients, a significantly elevated wavelet entropy was observed relative to controls for the whole cerebrum (P = 0.03). Spectral entropy (P = 0.41), regional homogeneity (P = 0.52), and Shannon entropy (P = 0.32) did not reveal significant differences.Conclusion: The wavelet entropy measure appeared more sensitive to detect abnormalities in cerebral fluctuations represented by nonstationary effects in the BOLD time series than more conventional measures. This effect was observed in the model time series as well as in Rolandic epilepsy. These observations suggest that the brains of children with Rolandic epilepsy exhibit stronger nonstationary temporal signal fluctuations than controls.Level Of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1728-1737. [ABSTRACT FROM AUTHOR]

Background Because acute ethanol (EtOH) administration is known to influence cognitive processes by impairing hippocampal function, electrophysiological responses of the hippocampus following EtOH exposure warrant investigation. To mimic in vivo conditions, we recorded and analyzed critical firing characteristics of the neuronal population dynamically, particularly in the hippocampal region, before and after acute EtOH administration. Methods Microelectrodes were inserted in the hippocampus CA1 region of 21 Institute of Cancer Research mice. The mice were divided into 3 groups, including an EtOH injection group (1.5 g/kg), a saline injection group (1.5 g/kg), and a negative control group that received no injection. A data acquisition system was employed to detect the local field potentials ( LFPs) and spike potentials following acute EtOH administration. Various multichannel electrophysiological signals were collected in vivo in each group at 60 minutes, from which the firing rate and wavelet entropy ( WE) were analyzed further. Results Firing rates began to decline at 20 minutes postinjection and then gradually recovered from 40 to 60 minutes. In contrast, 20 minutes post-injection, WE increased maximally and then returned to normal from 40 to 60 minutes ( p < 0.05). Pronounced changes in the relative energy of theta and alpha oscillations were also observed after 20 minutes of alcohol exposure and recovery occurred by 60 minutes ( p < 0.05). Conclusions A major mechanism of EtOH's action on the hippocampus is neurotransmitter blocking in the form of excitatory neuron inhibition in vivo. Changes in hippocampal spikes coincided with changes in LFPs during the entire time course of acute EtOH administration. The correlation between spikes and LFPs suggests that they jointly affect encoding in hippocampus. [ABSTRACT FROM AUTHOR]

An accurate diagnosis is important for the medical treatment of patients suffering from brain diseases. Magnetic resonance (MR) images are commonly used by technicians to assist preclinical diagnosis. The classification of MR images of normal and pathological brains poses a challenge from the technological point of view, since MR imaging generates a large information set that reflects the conditions of the brain. In this paper, we present a computer-assisted diagnosis method based on wavelet entropy (WE) of the feature space approach and a feed-forward neural network (FNN) classification method for improving the brain diagnosis accuracy by means of MR images. The most relevant image feature is selected as the WE, which is used to train an FNN classifier. The results using tenfold cross validation of 64 images show that the average accuracy attainable is 100.00%. It can be easily seen from the data that the proposed classifier can detect abnormal brains from normal controls with excellent performance, which can compete with the latest methods. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

We propose a novel approach for detecting precursors to epileptic seizures in intracranial electroencephalograms (i EEGs), which is based on the analysis of system dynamics. In the proposed scheme, the largest Lyapunov exponent (LLE) of wavelet entropy of the segmented EEG signals are considered as the discriminating features. Such features are processed by a support vector machine classifier, whose outcomes (the label and its probability for each LLE) are post‐processed and fed into a novel decision function to determine whether the corresponding segment of the EEG signal contains a precursor to an epileptic seizure. The proposed scheme is applied to the Freiburg data set, and the results show that seizure precursors are detected in a time frame that unlike other existing schemes is very much convenient to patients, with the sensitivity of 100% and negligible false positive detection rates. [ABSTRACT FROM AUTHOR]

ABSTRACT A novel millimeter-wave radar speech sensor based on vibration detection developed in our laboratory can penetrate nonmetal barriers to obtain speech signals, which expands traditional speech detection techniques. However, the radar speech signals are usually corrupted by nonuniform noises that include radar noise, electrocircuit noise, and so on. The article, therefore, proposed an improved Wiener filter method based on wavelet entropy (WE) to suppress the nonuniform noise. The utilization of WE contributes to the high efficiency of the algorithm for suppressing nonuniform radar noise. WE point detection can provide a basis through which radar noise can be accurately estimated and updated in an adaptive manner, resulting in accurate a priori signal-to-noise ratios obtained from updated noise estimations and reduction of nonuniform noise. The results from spectrogram and mean opinion score demonstrate that the radar nonuniform noise can be reduced efficiently, and the radar speech quality was improved effectively. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:1184-1189, 2014 [ABSTRACT FROM AUTHOR]

Neurofeedback training (NFT) has an important role in improvement of cognitive functions in both clinical and healthy individuals. In this study, variation of wavelet entropy during low beta NFT was investigated. To investigate the effect of low beta NFT on wavelet entropy, correlation between the change in low beta activity and the change in wavelet entropy was computed. The results revealed that there is a highly significant negative correlation between the change in low beta activity and wavelet entropy. The given outcome suggests that enhancing low beta activity through NFT associated with decrements in wavelet entropy. Furthermore, we discuss a new implementation of NFT, based on wavelet entropy for future research. © 2012 Wiley Periodicals, Inc. Complexity, 2012 [ABSTRACT FROM AUTHOR]

An expert system is presented for interpretation of the Doppler signals of heart valve diseases based on pattern recognition. We deal in particular with the combination of feature extraction and classification from measured Doppler signal waveforms at the heart valve using Doppler ultrasound. A wavelet neural network model developed by us is used. The model consists of two layers: a wavelet layer and a multilayer perceptron. The wavelet layer used for adaptive feature extraction in the time–frequency domain is composed of wavelet decomposition and wavelet entropy. The multilayer perceptron used for classification is a feedforward neural network. The performance of the developed system has been evaluated in 215 samples. The test results show that this system is effective to detect Doppler heart sounds. The classification rate averaged 91% correct for 123 test subjects. [ABSTRACT FROM AUTHOR]