Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review.

BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how machine learning has been used to develop algorithms to assess auditory brainstem responses. A clear and comprehensive overview is provided to allow clinicians and researchers to explore the domain and the potential translation to clinical care. METHODS: The systematic review was performed based on PRISMA guidelines. A search was conducted of PubMed, IEEE-Xplore, and Scopus databases focusing on human studies that have used machine learning to assess auditory brainstem responses. The duration of the search was from January 1, 1990, to April 3, 2021. The Covidence systematic review platform (www.covidence.org) was used throughout the process. RESULTS: A total of 5812 studies were found through the database search and 451 duplicates were removed. The title and abstract screening process further reduced the article count to 89 and in the proceeding full-text screening, 34 articles met our full inclusion criteria. CONCLUSION: Three categories of applications were found, namely neurologic diagnosis, hearing threshold estimation, and other (does not relate to neurologic or hearing threshold estimation). Neural networks and support vector machines were the most commonly used machine learning algorithms in all three categories. Only one study had conducted a clinical trial to evaluate the algorithm after development. Challenges remain in the amount of data required to train machine learning models. Suggestions for future research avenues are mentioned with recommended reporting methods for researchers.

Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory Processing Disorder.

INTRODUCTION: Auditory brainstem responses (ABRs) offer a unique opportunity to assess the neural integrity of the peripheral auditory nervous system in individuals presenting with listening difficulties. ABRs are typically recorded and analyzed by an audiologist who manually measures the timing and quality of the waveforms. The interpretation of ABRs requires considerable experience and training, and inappropriate interpretation can lead to incorrect judgments about the integrity of the system. Machine learning (ML) techniques may be a suitable approach to automate ABR interpretation and reduce human error. OBJECTIVES: The main objective of this paper was to identify a suitable ML technique to automate the analysis of ABR responses recorded as a part of the electrophysiological testing in the Auditory Processing Disorder clinical test battery. METHODS: ABR responses recorded during routine clinical assessment from 136 children being evaluated for auditory processing difficulties were analyzed using several common ML algorithms: Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Gradient Boosting (GB), Extreme Gradient Boosting (Xgboost), and Neural Networks (NN). A variety of signal feature extraction techniques were used to extract features from the ABR waveforms as inputs to the ML algorithms. Statistical significance testing and confusion matrices were used to identify the most robust model capable of accurately identifying neurological abnormalities present in ABRs. RESULTS: Clinically significant features in the time-frequency representation of the signal were identified. The ML model trained using the Xgboost algorithm was identified as the most robust model with an accuracy of 92% compared to other models. CONCLUSION: The findings of the present study demonstrate that it is possible to develop accurate ML models to automate the process of analyzing ABR waveforms recorded at suprathreshold levels. There is currently no ML-based application to screen children with listening difficulties. Therefore, it is expected that this work will be translated into an evaluation tool that can be used by audiologists in the clinic. Furthermore, this work may aid future researchers in exploring ML paradigms to improve clinical test batteries used by audiologists in achieving accurate diagnoses.

Auditory Brainstem Responses in Children with Auditory Processing Disorder.

BACKGROUND: The ASHA recommends including electrophysiological measures in an auditory processing disorder (APD) assessment battery, but few audiologists do so, potentially because of limited published evidence for its utility. PURPOSE: This study compared the auditory brainstem responses (ABRs) of children with APD with age-matched children and adults. STUDY SAMPLE: This study retrospectively examined the records of 108 children suspected of APD (sAPD) who had click-evoked ABRs recorded as part of their clinical assessment. Twenty adults and 22 typically developing (TD) children were recruited as controls. DATA COLLECTION AND ANALYSIS: Click-evoked ABRs were recorded at slow (13.3 clicks/sec) and faster (57.7 clicks/sec) stimulation rates. ABRs were analyzed using typical clinical measures (latencies and interpeak intervals for waves I, III, and V) and using a model proposed by Ponton et al that offered a more detailed analysis of axonal conduction time and synaptic transmission delay. RESULTS: Both clinical measures and the Ponton model analysis showed no significant differences between TD children and adults. Children sAPD showed absolute latencies that were significantly prolonged when compared with adults but not when compared with TD children. But individual children sAPD showed clinically significant delays (>2 standard deviations of TD children's data). Examination of responses delineating axonal versus synaptic transmission showed significant delays in synaptic transmission in the group of children sAPD in comparison to TD children and adults. These results suggest that a significant portion of children with listening difficulties showed evidence of reduced or atypical brainstem functioning. Examining the responses for axonal and synaptic delays revealed evidence of a synaptic pattern of abnormalities in a significant portion (37.03%) of children sAPD. Such observations could provide objective evidence of factors potentially contributing to listening difficulties that are frequently reported in children identified with APD. CONCLUSIONS: Children sAPD often showed abnormalities in the ABR, suggesting a neurophysiologic origin of their reported difficulties, frequently originating at or before the first synapse. This study provides supportive evidence for the value of click-evoked ABRs in comprehensive auditory processing assessment batteries.

Spectral ripple discrimination in children with auditory processing disorder.

Objective: The purpose of this study was to examine developmental trends in spectral ripple discrimination (SRD) and to compare the performance of typically developing children to children with auditory processing disorder (APD). Study design: Cross-sectional study. Study sample: Fifteen children with APD, as well as 17 typically developing children and 14 adults reporting no listening or academic difficulties participated. Results: Typically developing children showed poor SRD thresholds compared to adults, indicating prolonged maturation of spectral shape recognition. Both typically developing children and APD children showed a maturational trend in SRD, but a General Linear Model fit to their thresholds showed that children with APD displayed SRD thresholds that were significantly poorer than those of typically developing children when controlling for age. This suggests that in APD children, SRD maturation lags behind typically developing children. Conclusion: Poor spectral ripple discrimination may explain some of the listening difficulties experienced by children with APD.