scholarly journals A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164104 ◽  
Author(s):  
Cui Su ◽  
Zhenhu Liang ◽  
Xiaoli Li ◽  
Duan Li ◽  
Yongwang Li ◽  
...  
Keyword(s):  
Permutation Entropy ◽  
Depth Of Anesthesia ◽  
Entropy Measures ◽  
Anesthesia Monitoring ◽  
On Line ◽  
Line Depth

scholarly journals Employing entropy measures to identify visitors in multi-occupancy environments

2020 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Home Environment ◽  
Daily Living ◽  
Wearable Sensors ◽  
Permutation Entropy ◽  
Care Workers ◽  
Entropy Measures ◽  
Novel Method ◽  
High Degree ◽  
Motion Detectors ◽  
To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

scholarly journals Average Variations of Photospheric FeI and FeII Line Parameters as Function of the Magnetic Filling Factor

1990 ◽  
Vol 138 ◽  
pp. 41-46
Author(s):  
P.N. Brandt ◽  
M. Steinegger
Keyword(s):  
Filling Factor ◽  
Line Strength ◽  
Blue Shift ◽  
Disk Center ◽  
Flux Tubes ◽  
On Line ◽  
Fourier Transform Spectra ◽  
Similar Depth ◽  
Line Depth ◽  
The Continuum

A series of 17 Fourier transform spectra taken at the McMath telescope near disk center in regions of different magnetic field strengths were analyzed. Applying a multi-variate regression analysis magnetic filling factors 0 < α ≥ 0.11 were determined. With α increasing from 0 to 0.11, line bisectors averaged over groups of lines of similar depth are found to show a blue shift decreasing from 0.35 km s–1 to nearly 0.1 km s–1, when referred to the MgI line λ5172.7å. The bisectors of FeII lines exhibit smaller blue shifts than FeI lines. The increase of bisector red shift near the continuum with increasing α, found earlier by Brandt and Solanki (1987), was confirmed and is tentatively interpreted as a manifestation of downdrafts in the vicinity of flux tubes (Deinzer et al., 1984).A significant increase of line width (typically between 3 and 8%, depending on line strength) and a decrease of line depth is found with increasing filling factor. For strong lines the equivalent width W shows no variation or a slight increase, while for the weaker lines a reduction of W between a few % and > 10% is found.

scholarly journals Clinical Electroencephalography for Anesthesiologists

2015 ◽  
Vol 123 (4) ◽  
pp. 937-960 ◽  
Author(s):  
Patrick L. Purdon ◽  
Aaron Sampson ◽  
Kara J. Pavone ◽  
Emery N. Brown
Keyword(s):  
Neural Circuits ◽  
Brain State ◽  
Anesthesia Care ◽  
Depth Of Anesthesia ◽  
State Monitoring ◽  
Inhaled Anesthetics ◽  
Anesthesia Monitoring ◽  
Brain States ◽  
Index Value ◽  
The Brain

Abstract The widely used electroencephalogram-based indices for depth-of-anesthesia monitoring assume that the same index value defines the same level of unconsciousness for all anesthetics. In contrast, we show that different anesthetics act at different molecular targets and neural circuits to produce distinct brain states that are readily visible in the electroencephalogram. We present a two-part review to educate anesthesiologists on use of the unprocessed electroencephalogram and its spectrogram to track the brain states of patients receiving anesthesia care. Here in part I, we review the biophysics of the electroencephalogram and the neurophysiology of the electroencephalogram signatures of three intravenous anesthetics: propofol, dexmedetomidine, and ketamine, and four inhaled anesthetics: sevoflurane, isoflurane, desflurane, and nitrous oxide. Later in part II, we discuss patient management using these electroencephalogram signatures. Use of these electroencephalogram signatures suggests a neurophysiologically based paradigm for brain state monitoring of patients receiving anesthesia care.

scholarly journals A review of entropy measures for uncertainty quantification of stochastic processes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985735 ◽  
Author(s):  
Alireza Namdari ◽  
Zhaojun (Steven) Li
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Uncertainty Quantification ◽  
Lithium Ion ◽  
Second Law Of Thermodynamics ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Permutation Entropy ◽  
Battery Capacity ◽  
Entropy Measures

Entropy is originally introduced to explain the inclination of intensity of heat, pressure, and density to gradually disappear over time. Based on the concept of entropy, the Second Law of Thermodynamics, which states that the entropy of an isolated system is likely to increase until it attains its equilibrium state, is developed. More recently, the implication of entropy has been extended beyond the field of thermodynamics, and entropy has been applied in many subjects with probabilistic nature. The concept of entropy is applicable and useful in characterizing the behavior of stochastic processes since it represents the uncertainty, ambiguity, and disorder of the processes without being restricted to the forms of the theoretical probability distributions. In order to measure and quantify the entropy, the existing probability of every event in the stochastic process must be determined. Different entropy measures have been studied and presented including Shannon entropy, Renyi entropy, Tsallis entropy, Sample entropy, Permutation entropy, Approximate entropy, and Transfer entropy. This review surveys the general formulations of the uncertainty quantification based on entropy as well as their various applications. The results of the existing studies show that entropy measures are powerful predictors for stochastic processes with uncertainties. In addition, we examine the stochastic process of lithium-ion battery capacity data and attempt to determine the relation between the changes in battery capacity over different cycles and two entropy measures: Sample entropy and Approximate entropy.

scholarly journals Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhixian Yang ◽  
Yinghua Wang ◽  
Gaoxiang Ouyang
Keyword(s):  
Fuzzy Inference System ◽  
Slow Wave Sleep ◽  
Fuzzy Inference ◽  
Permutation Entropy ◽  
Eeg Signals ◽  
Inference System ◽  
Control Subjects ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Background Eeg

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

scholarly journals Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2499 ◽  
Author(s):  
Yue Gu ◽  
Zhenhu Liang ◽  
Satoshi Hagihira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Activity ◽  
Permutation Entropy ◽  
Depth Of Anesthesia ◽  
Spectral Edge Frequency ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

scholarly journals An Evaluation of Entropy Measures for Microphone Identification

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1235
Author(s):  
Gianmarco Baldini ◽  
Irene Amerini
Keyword(s):  
A Priori ◽  
Approximate Entropy ◽  
Identification Accuracy ◽  
Permutation Entropy ◽  
Features Selection ◽  
Statistical Features ◽  
Audio Signals ◽  
Trade Off ◽  
Entropy Measures ◽  
Physical Features

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.

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