Tai Nguyen-Ky

Measuring and Reflecting Depth of Anesthesia Using Wavelet and Power Spectral Density

This paper evaluates depth of anesthesia (DoA) monitoring using a new index. The proposed method preconditions raw EEG data using an adaptive threshold technique to remove spikes and low-frequency noise. We also propose an adaptive window length technique to adjust the length of the sliding window. The information pertinent to DoA is then extracted to develop a feature function using discrete wavelet transform and power spectral density. The evaluation demonstrates that the new index reflects the patients transition from consciousness to unconsciousness with the induction of anesthesia in real time.

An Improved Detrended Moving-Average Method for Monitoring the Depth of Anesthesia

The detrended moving-average (DMA) method is a new approach to quantify correlation properties in nonstationary signals with underlying trends. This paper monitored the depth of anesthesia (DoA) using modified DMA (MDMA) method. MDMA provides a power-law relation between the fluctuation function F_MDMA(s) and the scale s: F_MDMA( s)αs^α, where α is the slope of F_MDMA( s) in the logarithm scale. We applied the MDMA to monitor the DoA by computing the scaling exponent Fα and F_min values. To validate the proposed method, we compared our results with the bispectral index (BIS) monitor. We found a close correlation between our results and BIS with r( F_min) = 0.9346, r²(F_min) = 0.9183, and r(F_α) = 0.9458, r² (F_α) = 0.8855. Our method reflects the state of consciousness of a patient undergoing general anesthesia faster than BIS as observed clinically. The minimum time delay between the BIS and F_min trends was 12 s and the maximum was 178 s. Furthermore, in the case of poor signal quality, our results agreed with clinical observation, which indicates that our method can accurately estimate a patients hypnotic state in such circumstances. Fα and F_min trends are responsive and their movement seems similar to changes in the clinical state of the patients.

Theoretical basis for identification of different anesthetic states based on routinely recorded EEG during operation

In this paper, we present a new method to identify anesthetic states based on routinely recorded electroencephalogram (EEG). The identification of anesthesia stages are conducted using fast Fourier transform (FFT) and modified detrended fluctuation analysis (DFA) method. Simulation results demonstrate that this new method can clearly discriminate all five anesthesia states: very deep anesthesia, deep anesthesia, moderate anesthesia, light anesthesia and awake.

Consciousness and Depth of Anesthesia Assessment Based on Bayesian Analysis of EEG Signals

This study applies Bayesian techniques to analyze EEG signals for the assessment of the consciousness and depth of anesthesia (DoA). This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The maximum a posterior (MAP) is applied to denoise the wavelet coefficients based on a shrinkage function. When the anesthesia states change from awake to light, moderate, and deep anesthesia, the MAP values increase gradually. Based on these changes, a new function BDoA is designed to assess the DoA. The new proposed method is evaluated using anesthetized EEG recordings and BIS data from 25 patients. The Bland-Alman plot is used to verify the agreement of BDoA and the popular BIS index. A correlation between BDoA and BIS was measured using prediction probability PK. In order to estimate the accuracy of DoA, the effect of sample n and variance τ on the maximum posterior probability is studied. The results show that the new index accurately estimates the patients hypnotic states. Compared with the BIS index in some cases, the BDoA index can estimate the patients hypnotic state in the case of poor signal quality.

Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques

This paper presents a new index to measure the hypnotic depth of anaesthesia (DoA) using EEG signals. This index is derived from applying combined Wavelet transform, eigenvector and normalisation techniques. The eigenvector method is first applied to build a feature function for six levels of coefficients in a discrete wavelet transform (DWT). The best Daubechies wavelet and their ranking value p are optimally determined to identify different states of anaesthesia. A statistic normalisation process is then carried out to re-scale data and compute the hypnotic depth of anaesthesia. Finally, a new function ZDoA is proposed to compute a DoA index which corresponds one of the five depths of anaesthesia states to very deep anaesthesia, deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Simulation results based on real anaesthetised EEGs demonstrate that the new index generally parallels the BIS index. In particular, the ZDoA index is often faster than the BIS index to react to the transition period between consciousness and unconsciousness for this data set. A Bland-Altman plot indicates a 95.23% agreement between the ZDoA and BIS indices. The ZDoA trend is responsive, and its movement is consistent with the clinically observed and recorded changes of the patients.

Nonlinear Optimisation Using Production Functions to Estimate Economic Benefit of Conjunctive Water Use for Multicrop Production

Uncertainty and shortages of surface water supplies, as a result of global climate change, necessitate development of groundwater in many canal commands. Groundwater can be expensive to pump, but provides a reliable supply if managed sustainably. Groundwater can be used optimally in conjunction with surface water supplies. The use of such conjunctive systems can significantly decrease the risk associated with a stochastic availability of surface water supply. However, increasing pumping cost due to groundwater drawdown and energy prices are key concerns. We propose an innovative nonlinear programing model for the optimisation of profitability and productivity in an irrigation command area, with conjunctive water use options. The model, rather than using exogenous yields and gross margins, uses crop water production and profit functions to endogenously determine yields and water uses, and associated gross margins, respectively, for various conjunctive water use options. The model allows the estimation of the potential economic benefits of conjunctive water use and derives an optimal use of regional level land and water resources by maximising the net benefits and water productivity under various physical and economic constraints, including escalating energy prices. The proposed model is applied to the Coleambally Irrigation Area (CIA) in southeastern Australia to explore potential of conjunctive water use and evaluate economic implication of increasing energy prices. The results show that optimal conjunctive water use can offer significant economic benefit especially at low levels of surface water allocation and pumping cost. The results show that conjunctive water use potentially generates additional AUD 57.3 million if groundwater price is the same as surface water price. The benefit decreases significantly with increasing pumping cost.

Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

This paper presents a modified detrended fluctuation analysis (MDFA) to improve the monitoring accuracy of the depth of anaesthesia (DoA). We first use MDFA to classify anaesthesia state levels into awake, light, moderate, deep and very deep states. Then we build up five zones using linear regression method from very deep anaesthesia state to awake state, corresponding with different box sizes. Finally, the Lagrange method is applied to compute the DoA. Comparing with the most popular Bispectral Index (BIS) method, our modified DFA method extends the ranges of the moderate anaesthesia, deep anaesthesia and very deep anaesthesia to provide more information about the DoA. This extension is very significant in the clinical perspective as these states are within the ranges for operations and need more attention. Simulation results demonstrate that the new technique monitors the DoA in all anaesthesia states accurately.

Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

This study proposes a novel index MLDoA to identify different anaesthetic states of a patient during surgery. Based on the new index MLDoA, the assessment of depth of anaesthesia (DoA) for a patient can be clearly monitored. Firstly, a modified Bayesian wavelet threshold is proposed to de-noise the electroencephalogram (EEG) signals. Secondly, the Hurst exponent is obtained to classify four states of anaesthesia: deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Finally, the index MLDoA is derived based on the Hurst exponent and maximum-likelihood function. The MLDoA index is evaluated using clinically obtained EEG signals and the bispectral (BIS) data. The results show that the new index remains robust in the case of poor signal quality where BIS does not. Moreover, the new index MLDoA responds faster than the BIS index during the anaesthetic state transitions of patients. To validate the proposed method, the analysis of variance method is used to compare the new index MLDoA with the BIS index. The results indicate that the MLDoA distribution is better in distinguishing the five DoA states.

De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients

In monitoring the depth of anaesthesia, raw EEG signals are recorded by means of an adhesive sensor attached to the forehead. The raw EEG signal is often corrupted by spike, low frequency and high frequency noise. Removal of such noise improves clinical utility and this paper presents a novel method which uses a double wavelet-based de-noising algorithm. The results of experimental simulations show that the proposed method reproduces the EEG signal almost noiselessly. The resultant data is suitable input for monitoring the depth of anaesthesia. We propose to build up a wavelet-based Depth of Anaesthesia (WDoA) based on discrete wavelet transform (DWT) and power spectral density (PSD) function. Findings give very close correlation between the WDoA and BIS Index values, through the whole scale from 100 to 0 with full recording time on patient. Simulation results demonstrate that this new index, WDoA, represents the DoA in all anaesthesia states reliably and accurately.

Monitoring the Depth of Anesthesia Using Discrete Wavelet Transform and Power Spectral Density

This method combines wavelet techniques and power spectral density to monitor the depth of anesthesia (DOA) based on simplified EEG signals. After decomposing electroencephalogram (EEG) signals, the power spectral density is chosen as a feature function for coefficients of discrete wavelet transform. By computing the mean and standard deviation of the power spectral density values, we can classify the EEG signals to three classes, corresponding with the BIS values of 0 to 40, 40 to 60, and 60 to 100. Finally, three linear functions (