scholarly journals EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Quan Liu ◽  
Yi-Feng Chen ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Time Scales ◽  
Multiscale Entropy ◽  
Expert Assessment ◽  
Data Series ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
New Approach ◽  
Before And After ◽  
Artificial Neural ◽  
Different Time Scales

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index’s sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

scholarly journals Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
George J. A. Jiang ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Hui-Hsun Huang ◽  
Jheng-Yan Lan ◽  
...  
Keyword(s):  
Sample Entropy ◽  
Expert Assessment ◽  
Ann Model ◽  
Eeg Signals ◽  
Multivariate Empirical Mode Decomposition ◽  
Consciousness Level ◽  
Mode Decomposition ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

scholarly journals Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery

Entropy ◽  
2012 ◽  
Vol 14 (6) ◽  
pp. 978-992 ◽  
Author(s):  
Quan Liu ◽  
Qin Wei ◽  
Shou-Zen Fan ◽  
Cheng-Wei Lu ◽  
Tzu-Yu Lin ◽  
...  
Keyword(s):  
Multiscale Entropy ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
Adaptive Computation

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

2018 ◽  
Vol 63 (4) ◽  
pp. 481-490 ◽  
Author(s):  
Lal Hussain ◽  
Wajid Aziz ◽  
Sharjil Saeed ◽  
Saeed Arif Shah ◽  
Malik Sajjad A. Nadeem ◽  
...  
Keyword(s):  
Time Scales ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Operating Characteristics ◽  
Eeg Signals ◽  
Temporal Scales ◽  
Online Application ◽  
Time And Space Complexity

Abstract In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

scholarly journals Investigation of Spatial and Temporal Variability of Hydrological Drought in Slovenia Using the Standardised Streamflow Index (SSI)

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3197
Author(s):  
Lenka Zalokar ◽  
Mira Kobold ◽  
Mojca Šraj
Keyword(s):  
Time Scales ◽  
Temporal Variability ◽  
Hydrological Drought ◽  
Data Series ◽  
Spatial And Temporal Variability ◽  
Small Country ◽  
Discharge Data ◽  
Monthly Discharge ◽  
Gauging Station ◽  
Different Time Scales

Drought is a complex phenomenon with high spatial and temporal variability. Water scarcity has become a growing problem in Slovenia in recent decades. Therefore, the spatial and temporal variability of hydrological drought was investigated in this study by analysing the Standardized Streamflow Index (SSI). Monthly discharge data series from 46 gauging stations for the period 1961–2016 were used to calculate SSI values at five different time scales (1, 3, 6, 12, and 24 months). The results indicate that the frequency and intensity of droughts in Slovenia has increased in recent decades at most of the analysed gauging stations and at all time scales considered. Spring and summer periods were identified as critical in terms of water deficit. SSI values vary independently from the location of the gauging station, confirming that drought is a regional phenomenon, even in a small country such as Slovenia. However, SSI values vary considerably depending on the time scale chosen. This was also confirmed by the results of the hierarchical clustering of the number of extreme droughts, as various time scales resulted in a different distribution of gauging stations by individual groups.

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

A new approach to treatment of stomatitis in experiments on white rats (experimental research)

2018 ◽  
pp. 168-173
Author(s):  
Е.Б. Ганина ◽  
Ю.В. Червинец ◽  
Н.В. Грудинин ◽  
В.Г. Шестакова ◽  
В.М. Червинец ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Oral Cavity ◽  
Experimental Research ◽  
Quantitative Composition ◽  
Oral Diseases ◽  
New Approach ◽  
Highly Active ◽  
White Rats ◽  
Before And After ◽  
Intact Rats

Цель исследования - охарактеризовать микробиологическую картину стоматита до и после его лечения высокоактивными культурами лактобацилл, дать оценку эффективности их применения при экспериментальном стоматите. Методика. Опыты проводились на 32 самках беспородных белых крыс массой 230 г. Моделирование стоматита включало 2 этапа: моделирование травматического стоматита и воспроизведение бактериального стоматита на базе травматического. У крыс контрольной и опытных серий на всех этапах эксперимента брали мазки с поверхности десен для характеристики микробиоценоза ротовой полости. Результаты. После обработки слизистой оболочки полости рта (СОПР) культурой патогенного штамма Staphylococcus aureus существенно снизились частота встречаемости и количество условно-патогенных микроорганизмов. Лечение стоматита у крыс культурами лактобацилл ( Lactobacillus 11 зв., Lactobacillus 2 п.рта, Lactobacillus 24 д.ст.) и их комбинацией приводило к снижению распространенности S. aureus вплоть до его исчезновения, а также к нормализации количества условно-патогенной микробиоты. Заключение. Исследования показали эффективность использования отдельных высокоактивных штаммов лактобацилл или их комбинации при лечении заболеваний СОПР, в частности бактериального стоматита. Aim. To characterize the microbiological picture of stomatitis in intact rats before and after the treatment with highly active cultured lactobacilli and to evaluate the effectiveness of this treatment in experiments on white rats. Methods. Experiments were carried out on 32 mongrel female white rats weighing 230 g. Smears were taken from the gum surface of control and experimental rats at all stages of the experiment to characterize the oral cavity microbiocenosis. Results. After treatment of the oral cavity with cultured Staphylococcus aureus , the occurrence and quantity of opportunistic microorganisms significantly decreased. The treatment of stomatitis in rats with cultured lactobacillus ( Lactobacillus 11 zv., Lactobacillus 2 p.r., Lactobacillus 24 d.st. and their combination) led to a decrease in S. aureus prevalence up to its extinction and to normalization of the quantitative composition of opportunistic microbiota. Conclusions. The study showed the effieacy of highly active lactobacillus strains individually or their combinations in the treatment of oral diseases, in particular, bacterial stomatitis.

New Approach for 3D Mesh Retrieval Using Artificial Neural Network and Histogram of Features

2018 ◽  
Vol 10 (2) ◽  
pp. 84-94 ◽  
Author(s):  
M. Pershina ◽  
V.S. Bouksim ◽  
K. Arhid ◽  
F.R. Zakani ◽  
M. Aboulfatah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
New Approach ◽  
3D Mesh ◽  
Artificial Neural
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