Seyed Vahab Shojaedini - Department of Biomedical Engineering, Iranian Research Organization for Science and Technology, Tehran, Iran.
Amir Salar Jafarpisheh - Department of Ergonomics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran
Nematollah Rouhbakhsh - Department of Audiology, School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran.
Mohsen Vahedi - Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Negar Amirian - Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Objectives: Automated Auditory Brainstem Responses (ABR) peak detection is a novel technique to facilitate the measurement of neural synchrony along the auditory pathway through the brainstem. Analyzing the location of the peaks in these signals and the time interval between them may be utilized either for analyzing the hearing process or detecting peripheral and central lesions in the human hearing system. Methods: In this paper, model-based signal processing is proposed to estimate the effective parameters of ABR signals. In this process, the biological parameters of the signal are assessed by utilizing a Finite Impulse Response (FIR) adaptive filter in which its adaptation procedure is performed based on the correntropy concept. The proposed method is applied on a set of ABR signals recorded in response to three stimuli of /da/, /ba/, and /ga/, and then its performances are compared with an existing state-of-the-art technique.  Results: The results show that the proposed method can significantly increase the accuracy of estimating the parameters in stable stimulations (/da/, /ba/) for major positive and negative peaks. This improvement is more significant (up to ۲-۳ times) for /ba/ stimulus and especially in major positive peaks. However, in other peaks, the improvements also occurred in smaller amounts. However, for unstable stimuli (/ga/), no significant improvement was achieved. Discussion: Increasing the accuracy performance of the proposed method for detecting the stable stimuli (while its performance remains unchanged) for detecting unstable stimuli indicates its effectiveness in automated clinical analysis of ABR signals.

Auditory brainstem response, Model-based signal processing, Correntropy, Estimation