Sample Entropy of Speed Power Spectrum as a Measure of Laparoscopic Surgical Instrument Trajectory Smoothness

2018 ◽  
Author(s):  
Andrew R. Hutchins ◽  
Roberto J. Manson ◽  
Sabino Zani ◽  
Brian P. Mann
Keyword(s):  
Power Spectrum ◽  
Sample Entropy ◽  
Surgical Instrument

scholarly journals A Novel Blind Signal Detector Based on the Entropy of the Power Spectrum Subband Energy Ratio

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 448
Author(s):  
Han Li ◽  
Yanzhu Hu ◽  
Song Wang
Keyword(s):  
Power Spectrum ◽  
Detection Method ◽  
Multinomial Distribution ◽  
Sample Entropy ◽  
Energy Ratio ◽  
Statistical Characteristics ◽  
Interval Probability ◽  
Blind Signal ◽  
Signal Detector ◽  
Subband Energy

In this paper, we present a novel blind signal detector based on the entropy of the power spectrum subband energy ratio (PSER), the detection performance of which is significantly better than that of the classical energy detector. This detector is a full power spectrum detection method, and does not require the noise variance or prior information about the signal to be detected. According to the analysis of the statistical characteristics of the power spectrum subband energy ratio, this paper proposes concepts such as interval probability, interval entropy, sample entropy, joint interval entropy, PSER entropy, and sample entropy variance. Based on the multinomial distribution, in this paper the formulas for calculating the PSER entropy and the variance of sample entropy in the case of pure noise are derived. Based on the mixture multinomial distribution, the formulas for calculating the PSER entropy and the variance of sample entropy in the case of the signals mixed with noise are also derived. Under the constant false alarm strategy, the detector based on the entropy of the power spectrum subband energy ratio is derived. The experimental results for the primary signal detection are consistent with the theoretical calculation results, which proves that the detection method is correct.

scholarly journals EEG Microstate in Obstructive Sleep Apnea Patients

2020 ◽  
Author(s):  
Xin Xiong ◽  
Yuyan Ren ◽  
Shenghan Gao ◽  
Jianhua Luo ◽  
Jiangli Liao ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Power Spectrum ◽  
Transient State ◽  
Sample Entropy ◽  
Fluctuation Analysis ◽  
Obstructive Sleep ◽  
Healthy Control ◽  
Detrended Fluctuation ◽  
Electroencephalogram Eeg

Abstract Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstate reflecting the transient state of the brain is abnormal is unclear during sleep apnea or hypopnea. We investigated the microstates of sleep EEG in 30 OSA patients and in 10 healthy control volunteers. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). We observed that there was an additional fifth microstate E during apnea or hypopnea in N1 and N3 stages in OSA patients. And the microstate E was correlated with the power spectrum of delta, theta and alpha bands, not correlated with sample entropy, but correlated with DFA in N1-OA/OH stage. Moreover, Global Explained Variance, Mean Duration, Time Coverage and Segment Density of microstate E were positively correlated with DFA. We can interpret that the abnormal transition of brain active areas of OSA patients in N1-OA/OH stages leads to an extra microstate E, which might be related to the change of alpha activity in the cortex. And the generation of microstate E is not correlated with the decrease of EEG complexity, but correlated with the stronger self-similar regularity of EEG signals in OSA patients. These findings indicate that the microstate has the potential as a biomarker of EEG and has potential application value in OSA diagnosis.

scholarly journals Sample Entropy Tracks Changes in Electroencephalogram Power Spectrum With Sleep State and Aging

2009 ◽  
Vol 26 (4) ◽  
pp. 257-266 ◽  
Author(s):  
Eugene N. Bruce ◽  
Margaret C. Bruce ◽  
Swetha Vennelaganti
Keyword(s):  
Power Spectrum ◽  
Sample Entropy ◽  
Sleep State

Increased Speed Elicited More Automatized but Less Predictable Control in Cyclical Arm and Leg Movements

Motor Control ◽  
2021 ◽  
pp. 1-21
Author(s):  
Werner A.F. van de Ven ◽  
Jurjen Bosga ◽  
Wim Hullegie ◽  
Wiebe C. Verra ◽  
Ruud G.J. Meulenbroek
Keyword(s):  
Power Spectrum ◽  
Inertial Sensors ◽  
Arm Movements ◽  
Sample Entropy ◽  
Treadmill Walking ◽  
Task Demand ◽  
Entropy Measures ◽  
Linear Fit ◽  
Demand Changes ◽  
Leg Movements

The present study explores variations in the degree of automaticity and predictability of cyclical arm and leg movements. Twenty healthy adults were asked to walk on a treadmill at a lower-than-preferred speed, their preferred speed, and at a higher-than-preferred speed. In a separate, repetitive punching task, the three walking frequencies were used to cue the target pace of the cyclical arm movements. Movements of the arms, legs, and trunk were digitized with inertial sensors. Whereas absolute slope values (|β|) of the linear fit to the power spectrum of the digitized movements (p < .001, η2 = .676) were systematically smaller in treadmill walking than in repetitive punching, sample entropy measures (p < .001, η2 = .570) were larger reflecting the former task being more automated but also less predictable than the latter task. In both tasks, increased speeds enhanced automatized control (p < .001, η2 = .475) but reduced movement predictability (p = .008, η2 = .225). The latter findings are potentially relevant when evaluating effects of task demand changes in clinical contexts.

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Log-log scale roughness spectroscopy of surfaces

1993 ◽  
Vol 51 ◽  
pp. 224-225
Author(s):  
P. Fraundorf ◽  
B. Armbruster
Keyword(s):  
Power Spectrum ◽  
Autocorrelation Function ◽  
Power Spectra ◽  
Scanning Force Microscopy ◽  
Scanning Tunneling ◽  
Tunneling Microscopy ◽  
Force Microscopy ◽  
Scanning Force ◽  
Lateral Structure ◽  
Scale Roughness

Optical interferometry, confocal light microscopy, stereopair scanning electron microscopy, scanning tunneling microscopy, and scanning force microscopy, can produce topographic images of surfaces on size scales reaching from centimeters to Angstroms. Second moment (height variance) statistics of surface topography can be very helpful in quantifying “visually suggested” differences from one surface to the next. The two most common methods for displaying this information are the Fourier power spectrum and its direct space transform, the autocorrelation function or interferogram. Unfortunately, for a surface exhibiting lateral structure over several orders of magnitude in size, both the power spectrum and the autocorrelation function will find most of the information they contain pressed into the plot’s origin. This suggests that we plot power in units of LOG(frequency)≡-LOG(period), but rather than add this logarithmic constraint as another element of abstraction to the analysis of power spectra, we further recommend a shift in paradigm.

INSTABILITIES IN THE POWER SPECTRUM OF MODE-LOCKED SEMICONDUCTOR LASERS

1988 ◽  
Vol 49 (C2) ◽  
pp. C2-405-C2-408 ◽  
Author(s):  
D. BAUMS ◽  
M. SERÉNYI ◽  
W. ELSÄSSER ◽  
E. O. GÖBEL
Keyword(s):  
Power Spectrum ◽  
Semiconductor Lasers
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