Maximising the ability of stimulus-frequency otoacoustic emissions to predict hearing status and thresholds using machine-learning models

Abstract

Objective

This study aimed to maximise the ability of stimulus-frequency otoacoustic emissions (SFOAEs) to predict hearing status and thresholds based on machine-learning models.

Design

SFOAE data and audiometric thresholds were collected at octave frequencies from 0.5 to 8 kHz. Support vector machine, k-nearest neighbour, back propagation neural network, decision tree, and random forest algorithms were used to build classification models for status identification and to develop regression models for threshold prediction.

Study sample

About 230 ears with normal hearing and 737 ears with sensorineural hearing loss.

Results

All classification models yielded areas under the receiver operating characteristic curve of 0.926–0.994 at 0.5–8 kHz, superior to the previous SFOAE study. The regression models produced lower standard errors (8.1–12.2 dB, mean absolute errors: 5.53–8.97 dB) as compared to those for distortion-product and transient-evoked otoacoustic emissions previously reported (8.6–19.2 dB).

Conclusions

SFOAEs using machine-learning approaches offer promising tools for the prediction of hearing capabilities, at least at 0.5–4 kHz. Future research may focus on further improvements in accuracy and reductions in test time to improve clinical utility.

Keywords:

• Machine learning models
• stimulus-frequency otoacoustic emissions
• hearing status
• hearing thresholds
• principal component analysis

Acknowledgements

The authors thank Professor Mario Ruggero (Northwestern University) for help with text editing and Fei Ji (The General Hospital of the People’s Liberation Army) for help with data collection.

Disclosure statement

The authors have no conflicts of interest to disclose.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant number 61871252] and the National Natural Science Foundation of China [Grant number 61271133].