Optimized SNR-based ECAP threshold determination is comparable to the judgement of human evaluators

Area of Science:

• Neuroscience
• Biomedical Engineering
• Audiology

Background:

• Electrically evoked compound action potentials (ECAPs) confirm neuron-electrode interface function in cochlear implant (CI) users.
• Objective ECAP threshold measures are crucial for optimizing CI device fitting.
• Current threshold determination relies on subjective clinical assessment, highlighting the need for automated algorithms.

Purpose of the Study:

• To extend the signal-to-noise ratio (SNR) approach for automated ECAP threshold determination using Fine-Grain (FG) ECAP responses.
• To develop a computational algorithm aiding clinical specialists in objective ECAP threshold assessment.
• To improve the precision and efficiency of ECAP threshold measurement for CI users.

Main Methods:

• Applied a novel SNR-based algorithm to Fine-Grain (FG) ECAP responses for threshold determination.
• Utilized FG stimulation for enhanced recording resolution and SNR analysis for morphology-independent thresholds.
• Evaluated algorithm performance against manual assessments by experienced clinicians.

Main Results:

• The FG-SNR algorithm demonstrated high correlation (r = 0.78-0.93) with human evaluators' ECAP threshold determinations.
• Inter-evaluator agreement (r = 0.84-0.93) was comparable to the algorithm's agreement with evaluators.
• Denoising and precise time window determination were identified as key parameters influencing algorithm accuracy.

Conclusions:

• The developed FG-SNR algorithm provides a reliable and objective method for ECAP threshold determination in CI users.
• This automated approach has the potential to aid clinical decision-making and optimize CI fitting.
• The algorithm's adaptable parameters offer a framework for detecting other neurophysiological responses.