期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 40, 期 1, 页码 15-28出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.artmed.2006.06.003
关键词
cochlear implants; electrically evoked compound action potential; neural response telemetry; threshold estimation; automated systems; machine learning; pattern recognition; decision trees
Objective: AutoNRT((TM)) is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucteus((R)) Freedom((TM)) cochlear implant. ECAP thresholds along the electrode array are useful in objectively fitting cochlear implant systems for individual use. This paper provides the first detailed description of the AutoNRT algorithm and its expert systems, and reports the clinical success of AutoNRT to date. Methods: AutoNRT determines thresholds by visual detection, using two decision tree expert systems that automatically recognise ECAPs. The expert systems are guided by a dataset of 5393 neural response measurements. The algorithm approaches threshold from tower stimulus levels, ensuring recipient safety during postoperative measurements. Intraoperative measurements use the same algorithm but proceed faster by beginning at stimulus levels much closer to threshold. When searching for ECAPs, AutoNRT uses a highly specific expert system (specificity of 99% during training, 96% during testing; sensitivity of 91% during training, 89% during testing). Once ECAPs are established, AutoNRT uses an unbiased expert system to determine an accurate threshold. Throughout the execution of the algorithm, recording parameters (such as implant amplifier gain) are automatically optimised when needed. Results: In a study that included 29 intraoperative and 29 postoperative subjects (a total of 418 electrodes), AutoNRT determined a threshold in 93% of cases where a human expert also determined a threshold. When compared to the median threshold of multiple human observers on 77 randomly selected electrodes, AutoNRT performed as accurately as the 'average' clinician.
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