Evaluation of methods for the rapid extraction of the auditory brainstem response from underlying electroencephalogram

The single trial or rapid extraction of evoked potentials (EPs) has previously been applied to middle and late latency evoked potentials with the aim of accurately tracking a variety of central nervous system processes. Because the evoked ‘far fields’ are expected to be largely independent of the overlying ‘near field’ EEG noise, it can be argued that single trial extraction techniques are better suited to study rapid extraction of the auditory brain stem response (ABR) compared with the other EPs with cortical origin. However, methods have not been systematically studied to extract variations in the early ABR largely due to the inherent low signal to noise ratio in single trials. Therefore, this thesis aims to systematically analyse the denoising and time-scale variation tracking of the ABR using autoregression with an exogenous input (ARX) and wavelet methods. Rapid extraction of the ABR could reduce clinical test trial times, as a non-invasive tool for long-term patient monitoring systems with enhanced patient comfort and for real-time sensory identification applications in brain-computer interfacing. The literature revealed that, time-series modelling using ARX and wavelet denoising techniques have a potential to extract the ABR. These findings are further strengthened by the existence of commercial devices using ARX modelling for monitoring depth of anaesthesia and the encouraging results reported with wavelets in EP studies. The dissertation initially presents the analysis conducted to adopt ARX modelling to extract simulated ABRs. This includes a systematic evaluation of the ARX model and its modified algorithm; the robust evoked potential estimator (REPE), for their feasibility and limitations when used in the presence of known variations of ABR latency and signal to noise ratio. Results revealed superior performance with ARX modelling in extracted morphology (with a mean correlation coefficient of 0.84(SD =0.02)) and latency tracking (witha mean square error of 0.18(SD =0.02)) compared to the robust evoked potential estimator with a mean correlation coefficient of 0.63(SD =0.06) and a mean square error of 0.35(SD =0.06). Verification of these simulated results with actual ABRs concluded; while ARX modelling is capable of extracting time-scale varying features of a signal only at relatively high SNRs of > −20 dB. In a separate study, wavelet denoising methods were analysed as a rapid extraction system by initially applying them to simulated ABRs followed by application to ABRs recorded from human participants. The previously reported latency-intensity curve of the ABR wave V was used as the reference to determine the variation tracking capability of these wavelet methods. The application of the wavelet methods to the recorded ABRs required validation of threshold functions and time-windows as an integral part of this research. To arrive at more accurate results, the wavelet study was extended to observe the effect of shift-variant discrete wavelet transform and the shift-invariant stationary wavelet transform with the tested wavelet methods. It was revealed that the cyclic-shift-tree-denoising wavelet method with the discrete wavelet transform is the most effective since it produce significantly lower MSEs comparedto other methods (p< 0.01) and producing an optimum mean square error of 0.18 (SD =0.01). This required an ensemble of only 32 epochs to extract a fully featured ABR with latency variations associated with the latency-intensity curve. However, use of the computationally redundant stationary wavelet transform yielded significantly better results (p< 0.01) compared to the discrete wavelet transform with a MSE of 0.11 (SD =0.01). The resultant 32 epochs is a significant improvement compared to conventional moving time averaging which uses approximately 1024 epochs to extract the ABR. The systematic analysis of rapid extraction of the ABR concluded that CSTD wavelet method produced the optimum result with only an ensemble of 32 epochs to produce an ABR with characteristic features and their time-scale variations out performing ARX modelling methods. Future developments of this work could include recording the ABR in an ambulatory mode to document and understand the normal population, and such developments could also find subsequent clinical applications.