Objective: The aims were to 1) establish which of the four algorithms for estimating residual noise level and signal-to-noise ratio (SNR) in auditory brainstem responses (ABRs) perform better in terms of post-average wave-V peak latency and amplitude errors and 2) determine whether SNR or noise floor is a better stop criterion where the outcome measure is peak latency or amplitude. Design: The performance of the algorithms was evaluated by numerical simulations using an ABR template combined with electroencephalographic (EEG) recordings obtained without sound stimulus. The suitability of a fixed SNR versus a fixed noise floor stop criterion was assessed when variations in the wave-V waveform shape reflecting inter-subject variation was introduced. Study sample: Over 100 hours of raw EEG noise was recorded from 17 adult subjects, under different conditions (e.g. sleep or movement). Results: ABR feature accuracy was similar for the four algorithms. However, it was shown that a fixed noise floor leads to higher ABR wave-V amplitude accuracy; conversely, a fixed SNR yields higher wave-V latency accuracy. Conclusion: Similar performance suggests the use of the less computationally complex algorithms. Different stop criteria are recommended if the ABR peak latency or the amplitude is the outcome measure of interest.
- Auditory brainstem response
- Signal quality estimators
- Post-averaged noise estimators
- ABR latency and amplitude errors