Auditory Evoked Potential Detection During Pure-Tone Audiometry

Modern audiometry is largely a behavioural task, with the pure-tone audiogram (PTA) being the gold standard for evaluating frequency-specific hearing thresholds in adults. The nature of behavioural audiometry makes estimating accurate hearing thresholds difficult in infants and people with disabilities, where following instructions or interacting with the test may be difficult or impossible. We propose a method in which Auditory Evoked Potentials (AEPs) are used as an alternative to behavioural audiometry for detecting frequency-specific thresholds. Specifically, P300 responses elicited by the tones of a PTA are automatically detected from electroencephalogram (EEG) data, to evaluate hearing acuity. To assess the effectiveness of this method, we created a dataset of EEG recordings from participants presented with a series of pure tones at 6 different frequencies with steadily decreasing volumes, during a PTA test. This dataset was used to train a support vector machine (SVM) to identify when a participant was played a tone, whether they perceived it or not using their EEG. Results demonstrate that detecting hearing events can be very accurate for participants for whom the classifier has been trained a-priori. However, accuracy drops significantly for unseen participants - when the classifier has not been trained on any prior data from a given participant before classifying their EEG. However, by establishing that AEP response-based audiometry is viable for detecting tones, future work will explore the ability of more powerful deep neural networks to accurately estimate for unseen participants.