The result of auditory brainstem response is used worldwide for detecting hearing impairments or hearing aids. This study aimed to introduce the superiority of mathematical innovation algorithm toward subjective evaluation by an audiologist. The automatic algorithm method is encouraged for detecting the waves of Auditory Brainstem Response (ABR), because it can reduce subjective evaluation biases and visual analysis errors. This article portrays another technique for automatic detection of the peaks. Finally, by obtaining the standard pattern with this automatic algorithm for Persian speakers, we will compare it with the English speakers whose information was obtained by subjective method in Northwestern University. This article describes the effect of different factors on brainstem responses by performing a new automatic method. Methods: Auditory evoked potentials of brainstem activity were recorded by Electro encephalogram (EEG) of 27 Persian speaker adults with normal hearing. Three stimulus /ga/, / da/, and /ba/ were presented. This strategy depends on the utilization of reference wave forms, time latencies, and peaks adjusted and comparison with the ABR. Brainstem response latencies of brainstem peaks were extracted by the automatic method in temporal and spectral domains. This step provides language patterns for Persian speakers. Finally, the results of Persian speakers were compared with the results of a previous study done in Northwestern University by the same recording protocol as our own study on 22 English speaker children. Intraclass correlation coefficients and paired t test were used for evaluating and comparing the results. Results: According to the results, the performance of automatic method is high and reliable. Automatic and visual analysis methods had significant interaction. Latency of auditory brainstem response to the same stimulus in the two study groups was different and had a significant latency. The significance of these discoveries and clinical outcomes of this target strategy are featured in this paper. Discussion: This simple innovative algorithm could find the correct location of ABR peaks. Because of different acoustic signs and symptoms in the brainstem, the time latencies for all three stimulus used in this study are completely different.

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 2-3 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.