Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 15-28, May 2007

AutoNRT™: An automated system that measures ECAP thresholds with the Nucleus® Freedom™ cochlear implant via machine intelligence

  • Andrew Botros

      Affiliations

    • Cochlear Ltd., 14 Mars Road, Lane Cove, NSW 2066, Australia
    • Corresponding Author InformationCorresponding author. Tel.: +61 2 9428 6555; fax: +61 2 9428 6353.
  • ,
  • Bas van Dijk

      Affiliations

    • Cochlear Technology Centre Europe, Schaliënhoevedreef 20 I, 2800 Mechelen, Belgium
  • ,
  • Matthijs Killian

      Affiliations

    • Cochlear Technology Centre Europe, Schaliënhoevedreef 20 I, 2800 Mechelen, Belgium

Received 24 January 2006; received in revised form 11 May 2006; accepted 30 June 2006.

Summary 

Objective

AutoNRT™ is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucleus® Freedom™ 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 lower 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.

Conclusions

AutoNRT has demonstrated a high success rate and a level of performance that is comparable with human experts. It has been used in many clinics worldwide throughout the clinical trial and commercial launch of Nucleus Custom Sound™ Suite, significantly streamlining the clinical procedures associated with cochlear implant use.

Keywords: Cochlear implants, Electrically evoked compound action potential, Neural response telemetry, Threshold estimation, Automated systems, Machine learning, Pattern recognition, Decision trees

To access this article, please choose from the options below

Login to an existing account or Register a new account.

  • Purchase this article for 31.50 USD (You must login/register to purchase this article)

    Online access for 24 hours. The PDF version can be downloaded as your permanent record.

  • Subscribe to this title

    Get unlimited online access to this article and all other articles in this title 24/7 for one year.

  • Claim access now

    For current subscribers with Society Membership or Account Number.

  • Visit SciVerse ScienceDirect to see if you have access via your institution.
 

PII: S0933-3657(06)00099-6

doi:10.1016/j.artmed.2006.06.003

Artificial Intelligence in Medicine
Volume 40, Issue 1 , Pages 15-28, May 2007