Automatic analysis of auditory nerve electrically evoked compound action potential with an artificial neural network

Charasse, Basile; Thai-Van, Hung; Chanal, Jean Marc; Berger‐Vachon, Christian; Collet, Lionel

doi:10.1016/j.artmed.2004.03.004

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…One of the similarity-based approaches is to train artificial neural networks to respond to the similarity of a recording with sets of traces that do, or do not, contain a true response according to human experts. Charasse and colleagues 11 developed a neural network which, after training, found eCAP thresholds in new traces which on average differed from those identified by human experts by 3.6 clinical units-a smaller difference than that among the experts themselves. Another similarity method is to calculate the crosscorrelation product between the recording and templates that contain genuine responses.…”

Section: Automated Detectionmentioning

confidence: 99%

Advances in Cochlear Implant Telemetry: Evoked Neural Responses, Electrical Field Imaging, and Technical Integrity

Mens

2007

Trends in Amplification

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During the last decade, cochlear implantation has evolved into a well-established treatment of deafness, predominantly because of many improvements in speech processing and the controlled excitation of the auditory nerve. Cochlear implants now also feature telemetry, which is highly useful to monitor the proper functioning of the implanted electronics and electrode contacts. Telemetry can also support the clinical management in young children and difficult cases where neural unresponsiveness is suspected. This article will review recent advances in the telemetry of the electrically evoked compound action potential that have made these measurements simple and routine procedures in most cases. The distribution of the electrical stimulus itself sampled by "electrical field imaging" reveals general patterns of current flow in the normal cochlea and gross abnormalities in individual patients; models have been developed to derive more subtle insights from an individual electrical field imaging. Finally, some thoughts are given to the extended application of telemetry, for example, in monitoring the neural responses or in combination with other treatments of the deaf ear.

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Section: Automated Detectionmentioning

confidence: 99%

Advances in Cochlear Implant Telemetry: Evoked Neural Responses, Electrical Field Imaging, and Technical Integrity

Mens

2007

Trends in Amplification

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“…Here, we reviewed post-2015 AI applications in the field of otorhinolaryngology. Before 2015, most AI-based technologies focused on CIs [10,[75][76][77][78][79][80][81][82][83][84][85][86]. However, AI applications have expanded greatly in recent years.…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Tama

Kim

et al. 2020

Clin Exp Otorhinolaryngol

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Objectives. To present an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, with respect to opportunities, research challenges, and research directions.Methods. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles; we excluded non-English publications and duplicates, which resulted in a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments.Results. Most studies (42.22%, 38/90) used AI for image-based analysis, followed by clinical diagnosis and treatments (24 studies); each of the remaining two subcategories included 14 studies.Conclusion. Machine and deep learning have been extensively applied in the field of otorhinolaryngology. However, performance varies and research challenges remain.

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Programming – DBS Programming

Shils¹

2011

Essential Neuromodulation

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Automatic analysis of auditory nerve electrically evoked compound action potential with an artificial neural network

Cited by 9 publications

References 19 publications

Advances in Cochlear Implant Telemetry: Evoked Neural Responses, Electrical Field Imaging, and Technical Integrity

Advances in Cochlear Implant Telemetry: Evoked Neural Responses, Electrical Field Imaging, and Technical Integrity

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Programming – DBS Programming

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