Real-time threshold determination of auditory brainstem responses by cross-correlation analysis

Haoyu Wang; Bei Li; Yan Lu; Kun Han; Haibin Sheng; Jialei Zhou; Yumeng Qi; Xueling Wang; Zhiwu Huang; Lei Song; Yunfeng Hua

doi:10.1016/j.isci.2021.103285

Real-time threshold determination of auditory brainstem responses by cross-correlation analysis

iScience. 2021 Oct 14;24(11):103285. doi: 10.1016/j.isci.2021.103285. eCollection 2021 Nov 19.

Authors

Haoyu Wang^{1

2

3

4}, Bei Li¹, Yan Lu⁴, Kun Han¹, Haibin Sheng^{1

2

3}, Jialei Zhou⁵, Yumeng Qi⁴, Xueling Wang^{1

2

3}, Zhiwu Huang^{1

2

3}, Lei Song^{1

2

3}, Yunfeng Hua^{1

2

3

4}

Affiliations

¹ Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai, China.
² Ear Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
⁴ Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ Department of Otorhinolaryngology-Head & Neck Surgery, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Auditory brainstem response (ABR) serves as an objective indication of auditory perception at a given sound level and is nowadays widely used in hearing function assessment. Despite efforts for automation over decades, ABR threshold determination by machine algorithms remains unreliable and thereby one still relies on visual identification by trained personnel. Here, we described a procedure for automatic threshold determination that can be used in both animal and human ABR tests. The method terminates level averaging of ABR recordings upon detection of time-locked waveform through cross-correlation analysis. The threshold level was then indicated by a dramatic increase in the sweep numbers required to produce "qualified" level averaging. A good match was obtained between the algorithm outcome and the human readouts. Moreover, the method varies the level averaging based on the cross-correlation, thereby adapting to the signal-to-noise ratio of sweep recordings. These features empower a robust and fully automated ABR test.

Keywords: Algorithms; Sensory neuroscience; Techniques in neuroscience.