Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants

Abstract

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

Cochlear Implant Results in Older Adults with Post-Lingual Deafness: The Role of “Top-Down” Neurocognitive Mechanisms

“…However, despite these very good results with regards to its application, a wide intersubjective variability in the perception of language in CI older adult users [11] has been observed. The cochlear implantation outcome can be affected by multiple factors, such as the physical characteristics of the device (number of electrodes, frequency range, compression of the loudness dynamics, interaction between electrodes), the positioning of the device (depth of insertion, respect of tonotopy) and the clinical features of the implanted subject (age, date of onset and duration of deafness, cause of deafness and number of residual fibers of the auditory nerve) [12][13][14][15].…”

Application of Human Stem Cells to Model Genetic Sensorineural Hearing Loss and Meniere Disease

“…Despite the extensive occurrence of genetic SNHL in the world, there are no Food and Drug Administration (FDA)-approved cellular or molecular therapies [4,50]. Current treatments for human SNHL and MD are medical therapy using steroids, hearing aids, surgery to correct the cause of the hearing loss, or cochlear implants [84][85][86][87][88][89]. Though these devices offer significant relief of the moderate and severe SNHL by amplifying sound or directly electrically stimulating the auditory nerve, they have significant limitations in terms of speech discrimination in complex acoustic environments [90].…”

Application of Human Stem Cells to Model Genetic Sensorineural Hearing Loss and Meniere Disease

“…Despite the extensive occurrence of genetic SNHL in the world, there are no Food and Drug Administration (FDA)-approved cellular or molecular therapies [ 4 , 50 ]. Current treatments for human SNHL and MD are medical therapy using steroids, hearing aids, surgery to correct the cause of the hearing loss, or cochlear implants [ 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. Though these devices offer significant relief of the moderate and severe SNHL by amplifying sound or directly electrically stimulating the auditory nerve, they have significant limitations in terms of speech discrimination in complex acoustic environments [ 90 ].…”