A computerized database of 'normal' auditory brainstem responses

Automated wave labelling of the auditory brainstem response using machine learning

OBJECTIVE

To compare the performance of a selection of machine learning algorithms, trained to label peaks I, III, and V of the auditory brainstem response (ABR) waveform. An additional algorithm was trained to provide a confidence measure related to the ABR wave latency estimates.

DESIGN

Secondary data analysis of a previously published ABR dataset. Five types of machine learning algorithm were compared within a nested k-fold cross-validation procedure.

STUDY SAMPLE

A set of 482 suprathreshold ABR waveforms were used. These were recorded from 81 participants with audiometric thresholds within normal limits.

RESULTS

A convolutional recurrent neural network (CRNN) outperformed the other algorithms evaluated. The algorithm labelled 95.9% of ABR waves within ±0.1 ms of the target. The mean absolute error was 0.025 ms, averaged across the outer validation folds of the nested cross-validation procedure. High confidence levels were generally associated with greater wave-labelling accuracy.

CONCLUSIONS

Machine learning algorithms have the potential to assist clinicians with ABR interpretation. The present work identifies a promising machine learning approach, but any algorithm to be used in clinical practice would need to be trained on a large, accurately labelled, heterogeneous dataset and evaluated in clinical settings in follow-on work.

Automatic quality assessment and peak identification of auditory brainstem responses with fitted parametric peaks

The recording of the auditory brainstem response (ABR) is used worldwide for hearing screening purposes. In this process, a precise estimation of the most relevant components is essential for an accurate interpretation of these signals. This evaluation is usually carried out subjectively by an audiologist. However, the use of automatic methods for this purpose is being encouraged nowadays in order to reduce human evaluation biases and ensure uniformity among test conditions, patients, and screening personnel. This article describes a new method that performs automatic quality assessment and identification of the peaks, the fitted parametric peaks (FPP). This method is based on the use of synthesized peaks that are adjusted to the ABR response. The FPP is validated, on one hand, by an analysis of amplitudes and latencies measured manually by an audiologist and automatically by the FPP method in ABR signals recorded at different stimulation rates; and on the other hand, contrasting the performance of the FPP method with the automatic evaluation techniques based on the correlation coefficient, FSP, and cross correlation with a predefined template waveform by comparing the automatic evaluations of the quality of these methods with subjective evaluations provided by five experienced evaluators on a set of ABR signals of different quality. The results of this study suggest (a) that the FPP method can be used to provide an accurate parameterization of the peaks in terms of amplitude, latency, and width, and (b) that the FPP remains as the method that best approaches the averaged subjective quality evaluation, as well as provides the best results in terms of sensitivity and specificity in ABR signals validation. The significance of these findings and the clinical value of the FPP method are highlighted on this paper.