Abstract
In this paper, we apply the minimum variance estimation algorithm using a Kalman filter to the waveform data of auditory brainstem response (ABR). The model parameters that extract the feature are obtained effectively by our proposed method. The ABR is usually analyzed by the batch processing through addition for a few minutes because of its slight potential. We will expand the possibility of measuring it online by minimizing the model error based on a Kalman filter. We especially discuss the type and the degree of the estimated transfer function. We use the ABR waves obtained by averaging (traditional method) less than 2000 times and estimate its transfer function to extract the feature using a Kalman filter. The result of our method showed a higher correlation than traditional method of averaging to the ABR signal. If it is possible to apply this method to clinical cases, we can expect much reduction of the addition of signals which contributes to speed-up the monitor diagnoses in the emergency medical care and the operation, etc.
