Objective: The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters. ABR extraction method that can automatically adjust parameters to adapt different background noises was needed.
Methods: An adaptive Kalman filtering (AKF) based ABR signal extraction method was proposed, in which two recursive rules were introduced to constantly update the parameters according to the real-time noise properties. It was used for ABR extraction from recordings in noises with different orders of larger magnitude.
Results: The AKF method demonstrated the best performance in obtaining reliable ABR waveform morphologies in the presence of large EMG noises compared with traditional methods of TDA or KF. It could extract satisfactory ABR signal with fewer trials of acoustic stimulus repetition, even from noise 10000 times larger than ABR signal. The AKF results also showed smaller absolute errors and higher correlation coefficients with the target ABR signal when different types (gum chewing, mouth opening and milk drinking) or levels of noises were introduced.
Conclusion: The proposed AKF method is a great candidate to increase the robustness of current ABR measurements.
Significance: It could provide reduced testing time and relaxed recording conditions for ABR and other evoked potentials extraction.