Machine Learning in Automatic Recognition of Auditory Brainstem Response Waveforms
LIANG Sichao
XU Jia
YE Zuochang
LIU Haixu
LIANG Renhe
GUO Zhenping
LU Manlin
GAO JuanJuan
YI Haijin
Abstract：Objective The study aims to train various machine learning models for automated recognition of auditory brainstem response(ABR)waveforms to identify the model with the highest accuracy,thereby facilitating the application of automated ABR recognition technology in clinical practice.Methods The study included 100 participants(200 ears)recruited from Beijing Tsinghua Changgeng Hospital between June 2021 and June 2022,including individuals with normal hearing and those with hearing impairment.Pure-tone audiometry and ABR data were collected,and participants were divided into four groups based on age and hearing levels:i.e.18~59 years of age with normal hearing(average hearing threshold at 500,1000,2000,4000 Hz≤25 dB HL)(Group 1),≥60 years with normal hearing(Group 2),18~59 years with abnormal hearing(average hearing threshold>25 dB HL)(Group 3),and≥60 years with abnormal hearing(Group 4),with 25 subjects in each group.Time-domain and frequency-domain features of ABR signals were extracted and combined with participant demographics,pure-tone threshold,stimulus intensity and raw signal sequence to form comprehensive feature vectors.Eleven machine learning models were used for ABR waveform recognition,including logistic regression,support vector classification,Bernoulli Naive Bayes(BNB),Gaussian Naive Baye,Gaussian process classification,decision tree,random forest,tabular network,light gradient boosting machine,extreme gradient boosting,and local cascade ensemble.The recognition accuracy of each model was assessed for overall data and each group data.Results The Gaussian process classification model demonstrated the highest overall recognition accuracy(94.89%),which outperformed all other models.The subgroup accuracies were as follows:95.62%for normal hearing participants under 60 years old,92.19%for normal hearing participants aged 60 and above,92.92%for hearing-impaired participants under 60 years,and 92.50%for healing-impaired participants aged 60 and above.Conclusion Machine learning models demonstrates substantial potential for automated ABR waveforms recognition,with the Gaussian process classification model providing superior accuracy compared to other models,highlighting its potential for clinical implementation.
Keywords：auditory brainstem responsewaveform automatic recognitionmachine learningGaussian process classification model
Publication Date：2025-02-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 59-64 )