Estimation of Latency Changes and Relative Amplitudes in Somatosensory Evoked Potentials Using Wavelets and Regression

Changes in onset latency and relative amplitudes of somatosensory evoked potentials (SEP) may be a convenient and reliable neurophysiological indicator of depth of anesthesia. However, to derive the components is very difficult mathematically and visual inspection or alternatively the peak-latency estimation is usually employed. A methodology for estimating the components was developed for both real-time and off-line applications based on the combination of the wavelet transforms (WT), geometric analysis, artificial intelligence (AI), and mathematical analysis of the first positive wave of SEPs. The WT together with AI constitutes a feature extraction engine for localizing the first positive peak and negative valley and hence relative amplitudes. The latency change between two averages is obtained by shifting one average toward another to achieve a best match along the positive inflections. The inflection, based on the peak, is modeled as a regression line and is refined using a steepness inference algorithm. Results from simulation and anesthetized rats show that it is reliable in comparison with visual inspection, robust to amplitude variation and signal distortion, and efficient in computation, and hence it is suitable for automation. Comparisons of interobserver variability and analysis of method agreement suggest that the method can be used as a substitute for estimations by visual inspection.