Abstract
In this paper, we present an estimation algorithm for applying a Kalman filter to waveform data of the auditory brainstem response (ABR). The model parameters for extracting the features of the ABR waveform are obtained effectively by the proposed method. Usually, the ABR is analyzed by batch processing through signal averaging over a few minutes because the response has a slight potential. By using the Kalman filter, we expand the possibility of measuring it in real-time and minimizing the model error. First, we discuss, in particular, the type and the degree of the estimated transfer function. For this, we use the ABR waves obtained by adding less than 2000 click signals and estimate the transfer function in order to extract the features of the ABR waveform using the Kalman filter. Furthermore we analyze the characteristics of the coefficients of the transfer function in more detail to extract the ABR frequency characteristics. If we apply this method to clinical cases, we can expect that a significant reduction in the requirement for the addition of signals will contribute to reducing the burden for ABR monitoring in emergency medical care and surgical operations.
