Bayes factors for sequential auditory brainstem response detection

When determining the presence or absence of an Auditory Brainstem Response (ABR), clinicians often visually inspect the accruing data over time, i.e., a sequential test is adopted. The current work presents and evaluates Bayes Factors (BFs) as an objective sequential test for assisting clinicians with this task. Test specificity and sensitivity were optimised in simulated data and evaluated in subject-recorded data, including no-stimulus recordings (17 adults) and chirp-evoked ABR recordings (31 adults, 9 with hearing loss). The BF approach was compared with an existing sequential test, called the Convolutional Group Sequential Test (CGST). In simulations, BFs reduced mean test times by 60–70 % relative to the CGST while maintaining equal sensitivity and specificity. Similar reductions were observed in subject-recorded EEG background activity (∼70 %) and in chirp-evoked ABRs (0–60 %, depending on stimulus levels). For BFs, test time is tied to noise levels in the data, which allows test sensitivity to be controlled even when noise levels are high. The drawback is that the FPR is also tied to test time, and results show small variations (<0.01) in FPRs depending on noise levels. In contrast, test time for the CGST is fixed, giving an improved control over the FPR, but a reduced control over test sensitivity. BFs demonstrated high sensitivity and reduced mean test times relative to the CGST. It also provides regular feedback with no maximum test time specified, making it well-suited at assisting clinicians with different levels of expertise and feedback preferences.