Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

The electroencephalogram (EEG) signal from the brain is used for analysing brain abnormality, diseases, and monitoring patient conditions during surgery. One of the applications of the EEG signals analysis is real-time anaesthesia monitoring, as the anaesthetic drugs normally targeted the central nervous system. Depth of anaesthesia has been clinically assessed through breathing pattern, heart rate, arterial blood pressure, pupil dilation, sweating and the presence of movement. Those assessments are useful but are an indirect-measurement of anaesthetic drug effects. A direct method of assessment is through EEG signals because most anaesthetic drugs affect neuronal activity and cause a changed pattern in EEG signals. The aim of this research is to improve real-time anaesthesia assessment through EEG signal analysis which includes the filtering process, EEG features extraction and signal analysis for depth of anaesthesia assessment. The first phase of the research is EEG signal acquisition. When EEG signal is recorded, noises are also recorded along with the brain waves. Therefore, the filtering is necessary for EEG signal analysis. The filtering method introduced in this dissertation is Bayesian adaptive least mean square (LMS) filter which applies the Bayesian based method to find the best filter weight step for filter adaptation. The results show that the filtering technique is able to remove the unwanted signals from the EEG signals. This dissertation proposed three methods for EEG signal features extraction and analysing. The first is the strong analytical signal analysis which is based on the Hilbert transform for EEG signal features' extraction and analysis. The second is to extract EEG signal features using the Bayesian spike accumulation technique. The third is to apply the robust Bayesian Student-t distribution for real-time anaesthesia assessment. Computational results from the three methods are analysed and compared with the recorded BIS index which is the most popular and widely accepted depth of anaesthesia monitor. The outcomes show that computation times from the three methods are leading the BIS index approximately 18-120 seconds. Furthermore, the responses to anaesthetic drugs are verified with the anaesthetist's documentation and then compared with the BIS index to evaluate the performance. The results indicate that the three methods are able to extract EEG signal features efficiently, improve computation time, and respond faster to anaesthetic drugs compared to the existing BIS index

Removing noise from electroencephalogram signals for BIS based depth of anaesthesia monitors

The assessment of patient has changed from the physical assessment to digital assessment. One significant example is the assessment of the depth of anaesthesia (DoA). It has changed from physical to digital assessment using DoA monitor. DoA monitor uses the electroencephalogram (EEG) signal as its input. The processes include the digitising, filtering and signal analysing. This study focuses on filtering process to reduce noise in the EEG signal. Noises in EEG signals could affect the accuracy of DoA monitor. The noises in EEG signal are from the muscle, eye movement and blinking, power line, and interference with other device. Those noises are overlapped each other. Hence, monitoring of DoA without removing the noise may result in an incorrect assessment. A simple filtering process such as band pass filter is not able to remove all noise from EEG signals. There are three methods which are introduced to remove noise from EEG signals. The first technique is adaptive least mean square technique, which is able to find the best output of the signal through the iteration. In this method ANOVA technique is employed to define the best coefficient of the signal in the adaptation. This technique is chosen to find the significant output from the iteration. The result shows that the adaptive least mean square with the ANOVA is able to remove the noise from EEG signal effectively. second method is Wavelet transform. In this technique, EEG signal is decomposed into five levels using the Stationary Wavelet Transform (SWT). The first step of this filtering is to eliminate high frequency noise in the EEG signal. The next step is to remove the low frequency noise using the soft threshold method. The final step is to reconstruct the signal. The result from this method shows that there is a significant improvement of the signal quality after the filtering. The third method is a combination of adaptive LMS and wavelet transforms method. The result from this study shows that the wavelet transform adaptive filter is able to remove both the low frequency noise and high frequency noise in the EEG signal. Compare to the previous two other methods, the combined method is also more robust. Filtering the noise in EEG signal with wavelet transform adaptive filter technique could minimise false prediction of DoA

Real-time depth of anaesthesia assessment using strong analytical signal transform technique

This paper introduces a new method addressing depth of anaesthesia (DoA) assessment for real-time monitoring. The new method uses a combination of phase and amplitude of electroencephalogram (EEG) signals to assess the DoA level. A strong analytical signal transform is applied to extract the phase and amplitude information of the recorded EEG signals. Based on the extracted features from the EEG signal in each different frequency band, a new DoA index is developed. The proposed new DoA index is evaluated using data from adult patients in an age range from 22 to 75 years. The results show that the new DoA index is able to detect the changing pattern of EEG signals early and agree with the clinical notes of an attending anaesthetist. The results are also closely correlated with the popular BIS index. Furthermore, the proposed new DoA index is able to detect the state changes earlier than the BIS index

Investigation of Bispectral Index filtering and improvement using wavelet transform adaptive filter

Electroencephalogram (EEG) signals are often contaminated with artifacts such as electromyography (EMG), eye blink and eye ball movement. These contaminated EEG signals may give incorrect values of Bispectral Index. If fixed band-pass filter is used to filter the overlapping signals between the EEG and the artifacts, the useful information in EEG signal could be lost. This paper proposes a method to filter the EEG signals using wavelet adaptive techniques. The preliminary result shows that this technique is capable to remove the artifacts from the EEG signal more efficiently