Assessment of inter-examiner agreement and variability in the manual classification of auditory brainstem response

Abstract Background: The analysis of the Auditory Brainstem Response (ABR) is of fundamental importance to the investigation of the auditory system behaviour, though its interpretation has a subjective nature because of the manual process employed in its study and the clinical experience required for its analysis. When analysing the ABR, clinicians are often interested in the identification of ABR signal components referred to as Jewett waves. In particular, the detection and study of the time when these waves occur (i.e., the wave latency) is a practical tool for the diagnosis of disorders affecting the auditory system. Significant differences in inter-examiner results may lead to completely distinct clinical interpretations of the state of the auditory system. In this context, the aim of this research was to evaluate the inter-examiner agreement and variability in the manual classification of ABR. Methods: A total of 160 ABR data samples were collected, for four different stimulus intensity (80dBHL, 60dBHL, 40dBHL and 20dBHL), from 10 normal-hearing subjects (5 men and 5 women, from 20 to 52 years). Four examiners with expertise in the manual classification of ABR components participated in the study. The Bland-Altman statistical method was employed for the assessment of inter-examiner agreement and variability. The mean, standard deviation and error for the bias, which is the difference between examiners’ annotations, were estimated for each pair of examiners. Scatter plots and histograms were employed for data visualization and analysis. Results: In most comparisons the differences between examiner’s annotations were below 0.1 ms, which is clinically acceptable. In four cases, it was found a large error and standard deviation (>0.1 ms) that indicate the presence of outliers and thus, discrepancies between examiners. Conclusions: Our results quantify the inter-examiner agreement and variability of the manual analysis of ABR data, and they also allows for the determination of different patterns of manual ABR analysis

Detection of Auditory Brainstem Response Peaks Using Image Processing Techniques in Infants with Normal Hearing Sensitivity

Introduction: The auditory brainstem response (ABR) is measured to find the brainstem-level peripheral auditory nerve system integrity in children having normal hearing. The Auditory Evoked Potential (AEP) is generated using acoustic stimuli. Interpreting these waves requires competence to avoid misdiagnosing hearing problems. Automating ABR test labeling with computer vision may reduce human error. Method: The ABR test results of 26 children aged 1 to 20 months with normal hearing in both ears were used. A new approach is suggested for automatically calculating the peaks of waves of different intensities (in decibels). The procedure entails acquiring wave images from an Audera device using the Color Thresholder method, segmenting each wave as a single wave image using the Image Region Analyzer application, converting all wave images into waves using Image Processing (IP) techniques, and finally calculating the latency of the peaks for each wave to be used by an audiologist for diagnosing the disease. Findings: Image processing techniques were able to detect 1, 3, and 5 waves in the diagnosis field with accuracy (0.82), (0.98), and (0.98), respectively, and its precision for waves 1, 3, and 5, were respectively (0.32), (0.97) and (0.87). This evaluation also worked well in the thresholding part and 82.7 % correctly detected the ABR waves. Conclusion: Our findings indicate that the audiology test battery suite can be made more accurate, quick, and error-free by using technology to automatically detect and label ABR waves

A Model for Electrical Communication Between Cochlear Implants and the Brain

In the last thirty years, cochlear implants have become an invaluable instrument in the treatment of severe-to-profound hearing impairment. An important aspect of research in the continued development of cochlear implants is the in vivo assessment of signal processing algorithms intended to improve perception of speech and other auditory signals. In trying to determine how closely cochlear implant recipients process sound relative to the processing done by a normal auditory system, various assessment techniques have been applied. The most common technique has been measurement of auditory evoked potentials (AEPs), which involves the recording of neural responses to auditory stimulation. Depending on the latency of the observed response, the evoked potential indicates neural activity at various ascending neurological structures of the auditory system. Although there have been a number of publications on the topic of AEPs in cochlear implant subjects, there is a need for better measurement and research techniques to obtain more in-depth information to facilitate research on effectiveness of signal processing approaches in cochlear implants. The research presented herein explored the use of MatLab for the purpose of developing a model for electrically evoked auditory brainstem responses (EABRs). The EABR is commonly measured in hearing-impaired patients who have cochlear implants, via electrical stimulation delivered from electrodes in the implanted array. The simulation model developed in this study took as its input the stimulus current intensity level, and used function vectors and equations derived from measured EABRs, to generate an approximation of the evoked surface potentials. A function vector was used to represent the combined firing of the neurons of the auditory nervous system that are needed to elicit a measurable response. Equations were derived to represent the latency and stimulus amplitude scaling functions. The simulation also accounted for other neural activity that can be present in and contaminate an ABR recording, and reduced it through time-locked averaging of the simulated response. Predicted waveforms from the MatLab model were compared both to published waveforms from a cochlear implant recipient, and a series of EABR waveforms measured by the author in other cochlear implant recipients. Measurement of the EABRs required specialized interfacing of a commercial recording system with the signal processors of the patients\u27 cochlear implants. A novel measurement technique was also used to obtain more frequency-specific information than usually obtained. Although the nonlinearities normally present in the auditory system were not considered in this MatLab simulation, the model nevertheless performed well and delivered results comparing favorably with the results measured from the research subjects

Objective detection of brainstem auditory evoked potentials with a priori information from higher presentation levels

International audienceThis paper describes a brainstem auditory evoked potentials (BAEPs) detection method based on supervised pattern recognition. A previously used pattern recognition technique relying on cross-correlation with a template was modified in order to include a priori information allowing detection accuracy. Reference is made to the patient’s audiogram and to the latency–intensity (LI) curve with respect to physiological mechanisms. Flexible and adaptive constraints are introduced in the optimization procedure by means of eight rules. Several data samples were used in this study. The determination of parameters was performed through 270 BAEPs from 20 subjects with normal and high audiometric thresholds and through additional BAEPs from 123 normal ears and 14 ears showing prominent wave VI BAEPs. The evaluation of the detection performance was performed in two steps: first, the sensitivity, specificity and accuracy were estimated using 283 BAEPs from 20 subjects showing normal and high audiometric thresholds and secondly, the sensitivity, specificity and accuracy of the detection and the accuracy of the response threshold were estimated using 213 BAEPs from 18 patients in clinic.Taking into account some a priori information, the accuracy in BAEPs detection was enhanced from 76 to 90%. The patient response thresholds were determined with a mean error of 5 dB and a standard deviation error of 8.3 dB. Results were obtained using experimental data; therefore, they are promising for routine use in clinic