Biophysical modeling of a cochlear implant system: progress on closed-loop design using a novel patient-specific evaluation platform

The modern cochlear implant is one of the most successful neural stimulation devices, which partially mimics the workings of the auditory periphery. In the last few decades it has created a paradigm shift in hearing restoration of the deaf population, which has led to more than 324,000 cochlear implant users today. Despite its great success there is great disparity in patient outcomes without clear understanding of the aetiology of this variance in implant performance. Furthermore speech recognition in adverse conditions or music appreciation is still not attainable with today's commercial technology. This motivates the research for the next generation of cochlear implants that takes advantage of recent developments in electronics, neuroscience, nanotechnology, micro-mechanics, polymer chemistry and molecular biology to deliver high fidelity sound. The main difficulties in determining the root of the problem in the cases where the cochlear implant does not perform well are two fold: first there is not a clear paradigm on how the electrical stimulation is perceived as sound by the brain, and second there is limited understanding on the plasticity effects, or learning, of the brain in response to electrical stimulation. These significant knowledge limitations impede the design of novel cochlear implant technologies, as the technical specifications that can lead to better performing implants remain undefined. The motivation of the work presented in this thesis is to compare and contrast the cochlear implant neural stimulation with the operation of the physiological healthy auditory periphery up to the level of the auditory nerve. As such design of novel cochlear implant systems can become feasible by gaining insight on the question `how well does a specific cochlear implant system approximate the healthy auditory periphery?' circumventing the necessity of complete understanding of the brain's comprehension of patterned electrical stimulation delivered from a generic cochlear implant device. A computational model, termed Digital Cochlea Stimulation and Evaluation Tool (‘DiCoStET’) has been developed to provide an objective estimate of cochlear implant performance based on neuronal activation measures, such as vector strength and average activation. A patient-specific cochlea 3D geometry is generated using a model derived by a single anatomical measurement from a patient, using non-invasive high resolution computed tomography (HRCT), and anatomically invariant human metrics and relations. Human measurements of the neuron route within the inner ear enable an innervation pattern to be modelled which joins the space from the organ of Corti to the spiral ganglion subsequently descending into the auditory nerve bundle. An electrode is inserted in the cochlea at a depth that is determined by the user of the tool. The geometric relation between the stimulation sites on the electrode and the spiral ganglion are used to estimate an activating function that will be unique for the specific patient's cochlear shape and electrode placement. This `transfer function', so to speak, between electrode and spiral ganglion serves as a `digital patient' for validating novel cochlear implant systems. The novel computational tool is intended for use by bioengineers, surgeons, audiologists and neuroscientists alike. In addition to ‘DiCoStET’ a second computational model is presented in this thesis aiming at enhancing the understanding of the physiological mechanisms of hearing, specifically the workings of the auditory synapse. The purpose of this model is to provide insight on the sound encoding mechanisms of the synapse. A hypothetical mechanism is suggested in the release of neurotransmitter vesicles that permits the auditory synapse to encode temporal patterns of sound separately from sound intensity. DiCoStET was used to examine the performance of two different types of filters used for spectral analysis in the cochlear implant system, the Gammatone type filter and the Butterworth type filter. The model outputs suggest that the Gammatone type filter performs better than the Butterworth type filter. Furthermore two stimulation strategies, the Continuous Interleaved Stimulation (CIS) and Asynchronous Interleaved Stimulation (AIS) have been compared. The estimated neuronal stimulation spatiotemporal patterns for each strategy suggest that the overall stimulation pattern is not greatly affected by the temporal sequence change. However the finer detail of neuronal activation is different between the two strategies, and when compared to healthy neuronal activation patterns the conjecture is made that the sequential stimulation of CIS hinders the transmission of sound fine structure information to the brain. The effect of the two models developed is the feasibility of collaborative work emanating from various disciplines; especially electrical engineering, auditory physiology and neuroscience for the development of novel cochlear implant systems. This is achieved by using the concept of a `digital patient' whose artificial neuronal activation is compared to a healthy scenario in a computationally efficient manner to allow practical simulation times.Open Acces

Auf einem menschlichen Gehörmodell basierende Elektrodenstimulationsstrategie für Cochleaimplantate

Cochleaimplantate (CI), verbunden mit einer professionellen Rehabilitation, haben mehreren hunderttausenden Hörgeschädigten die verbale Kommunikation wieder ermöglicht. Betrachtet man jedoch die Rehabilitationserfolge, so haben CI-Systeme inzwischen ihre Grenzen erreicht. Die Tatsache, dass die meisten CI-Träger nicht in der Lage sind, Musik zu genießen oder einer Konversation in geräuschvoller Umgebung zu folgen, zeigt, dass es noch Raum für Verbesserungen gibt.Diese Dissertation stellt die neue CI-Signalverarbeitungsstrategie Stimulation based on Auditory Modeling (SAM) vor, die vollständig auf einem Computermodell des menschlichen peripheren Hörsystems beruht.Im Rahmen der vorliegenden Arbeit wurde die SAM Strategie dreifach evaluiert: mit vereinfachten Wahrnehmungsmodellen von CI-Nutzern, mit fünf CI-Nutzern, und mit 27 Normalhörenden mittels eines akustischen Modells der CI-Wahrnehmung. Die Evaluationsergebnisse wurden stets mit Ergebnissen, die durch die Verwendung der Advanced Combination Encoder (ACE) Strategie ermittelt wurden, verglichen. ACE stellt die zurzeit verbreitetste Strategie dar. Erste Simulationen zeigten, dass die Sprachverständlichkeit mit SAM genauso gut wie mit ACE ist. Weiterhin lieferte SAM genauere binaurale Merkmale, was potentiell zu einer Verbesserung der Schallquellenlokalisierungfähigkeit führen kann. Die Simulationen zeigten ebenfalls einen erhöhten Anteil an zeitlichen Pitchinformationen, welche von SAM bereitgestellt wurden. Die Ergebnisse der nachfolgenden Pilotstudie mit fünf CI-Nutzern zeigten mehrere Vorteile von SAM auf. Erstens war eine signifikante Verbesserung der Tonhöhenunterscheidung bei Sinustönen und gesungenen Vokalen zu erkennen. Zweitens bestätigten CI-Nutzer, die kontralateral mit einem Hörgerät versorgt waren, eine natürlicheren Klangeindruck. Als ein sehr bedeutender Vorteil stellte sich drittens heraus, dass sich alle Testpersonen in sehr kurzer Zeit (ca. 10 bis 30 Minuten) an SAM gewöhnen konnten. Dies ist besonders wichtig, da typischerweise Wochen oder Monate nötig sind. Tests mit Normalhörenden lieferten weitere Nachweise für die verbesserte Tonhöhenunterscheidung mit SAM.Obwohl SAM noch keine marktreife Alternative ist, versucht sie den Weg für zukünftige Strategien, die auf Gehörmodellen beruhen, zu ebnen und ist somit ein erfolgversprechender Kandidat für weitere Forschungsarbeiten.Cochlear implants (CIs) combined with professional rehabilitation have enabled several hundreds of thousands of hearing-impaired individuals to re-enter the world of verbal communication. Though very successful, current CI systems seem to have reached their peak potential. The fact that most recipients claim not to enjoy listening to music and are not capable of carrying on a conversation in noisy or reverberative environments shows that there is still room for improvement.This dissertation presents a new cochlear implant signal processing strategy called Stimulation based on Auditory Modeling (SAM), which is completely based on a computational model of the human peripheral auditory system.SAM has been evaluated through simplified models of CI listeners, with five cochlear implant users, and with 27 normal-hearing subjects using an acoustic model of CI perception. Results have always been compared to those acquired using Advanced Combination Encoder (ACE), which is today’s most prevalent CI strategy. First simulations showed that speech intelligibility of CI users fitted with SAM should be just as good as that of CI listeners fitted with ACE. Furthermore, it has been shown that SAM provides more accurate binaural cues, which can potentially enhance the sound source localization ability of bilaterally fitted implantees. Simulations have also revealed an increased amount of temporal pitch information provided by SAM. The subsequent pilot study, which ran smoothly, revealed several benefits of using SAM. First, there was a significant improvement in pitch discrimination of pure tones and sung vowels. Second, CI users fitted with a contralateral hearing aid reported a more natural sound of both speech and music. Third, all subjects were accustomed to SAM in a very short period of time (in the order of 10 to 30 minutes), which is particularly important given that a successful CI strategy change typically takes weeks to months. An additional test with 27 normal-hearing listeners using an acoustic model of CI perception delivered further evidence for improved pitch discrimination ability with SAM as compared to ACE.Although SAM is not yet a market-ready alternative, it strives to pave the way for future strategies based on auditory models and it is a promising candidate for further research and investigation

Optimizing Stimulation Strategies in Cochlear Implants for Music Listening

Most cochlear implant (CI) strategies are optimized for speech characteristics while music enjoyment is signicantly below normal hearing performance. In this thesis, electrical stimulation strategies in CIs are analyzed for music input. A simulation chain consisting of two parallel paths, simulating normal hearing conditions and electrical hearing respectively, is utilized. One thesis objective is to congure and develop the sound processor of the CI chain to analyze dierent compression- and channel selection strategies to optimally capture the characteristics of music signals. A new set of knee points (KPs) for the compression function are investigated together with clustering of frequency bands. The N-of-M electrode selection strategy models the eect of a psychoacoustic masking threshold. In order to evaluate the performance of the CI model, the normal hearing model is considered a true reference. Similarity among the resulting neurograms of respective model are measured using the image analysis method Neurogram Similarity Index Measure (NSIM). The validation and resolution of NSIM is another objective of the thesis. Results indicate that NSIM is sensitive to no-activity regions in the neurograms and has diculties capturing small CI changes, i.e. compression settings. Further verication of the model setup is suggested together with investigating an alternative optimal electric hearing reference and/or objective similarity measure

The Application of a Piezoelectric MEMS Cantilever Array as a Completely Implantable Cochlear Implant.

Aluminum nitride (AlN) is an excellent material for MEMS sensors because of its low dissipation factor, high resistance, and relatively high piezoelectric coefficients. We present an array of AlN bimorph cantilevers, fabricated using MEMS batch fabrication, which has applications as a completely implantable cochlear implant (CI). Unlike traditional CIs, this probe is designed to locally transduce mechanical vibrations of the cochlear fluid into electrical signals that stimulate the auditory nerves. A silicon backbone supports an array of five cantilevers that have a range of resonances spanning 20-40kHz in water. Fabricated cabling extends from the probe for external monitoring during in vitro and in vivo studies. We present characterization of the effect of deposition power on the growth of 1.5μm thick AlN films grown on 565nm of thermal oxide. X-ray diffraction analysis of the films indicates well-ordered, c-axis oriented growth. High resolution transmission electron microscope imaging was used to identify atomic-level characteristics of process induced faults and dislocations occurring at the start of deposition and when vacuum is broken between two consecutive AlN depositions. Initial acute in vivo testing of an implanted fabricated device produced a 2μV device response when a 110dBSPL sound source was played at the entrance to the ear canal, demonstrating that this device has the potential to restore hearing with sufficient amplification. Benefits of this design include lower power, smaller size, and lower latency when compared with current commercial CIs.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110503/1/kknisely_1.pd

Gain optimization for cochlear implant systems

Cochlear implant systems need Automatic Gain Control (AGC) to compress the large dynamic range (~120 dB) of the acoustic environment into the small dynamic range (< 20 dB) of electrical stimulation. This thesis is concerned with the design, implementation and evaluation of AGC systems for cochlear implants. It investigated the effects of AGC on the speech intelligibility of cochlear implant recipients. Various AGC configurations were evaluated with sentences presented over a wide range of levels at different Signal-to-Noise Ratios (SNR) to identify important factors affecting the performance. Signal metrics were developed to quantify the effects of AGC on the channel envelopes. The goal was to improve speech intelligibility in adverse listening conditions. The performance-intensity functions of cochlear implant recipients with no AGC and with a front-end compression limiter were measured in noise. With no AGC, the proportion of envelope clipping grew monotonically with presentation level. The front-end limiter substantially reduced envelope clipping yet gave little improvement in speech intelligibility. The recipients were highly tolerant of envelope clipping when the background noise was low. SNR degradation was identified as the main factor reducing speech intelligibility. A front-end limiter cannot guarantee zero envelope clipping. In contrast, the proposed envelope profile limiter eliminated envelope clipping and hence preserved the spectral profile. The two AGCs were evaluated, with two release times (75 and 625 ms). The shorter release time gave worse speech intelligibility because it caused more waveform distortion and output SNR reduction. For a given release time, preserving spectral envelope profile gave additional benefits. In a take-home experiment, cochlear implant recipients rated a program with the envelope profile limiter equivalent to their everyday program. A conventional cochlear implant signal path uses a predetermined input dynamic range, which is shifted up or down by the AGC. In contrast, the proposed Adaptive Loudness Growth Function (ALGF) continually optimized the input dynamic range by estimating the noise floor and peak level in each channel. The ALGF gave better Speech Reception Threshold (SRT) than the existing state-of-the-art AGC system at the high presentation level when evaluated with a newly developed roving-level SRT test at three presentation levels

Active surgical positioning device for a cochlear implant electrode array

Cochlear implants have been of great benefit in restoring auditory function to individuals with profound bilateral sensorineural deafness. The implants are used to directly stimulate auditory nerves and send a signal to the brain that is then interpreted as sound. This project focuses on the development of a surgical positioning tool to accurately and effectively place an array of stimulating electrodes deep within the cochlea. This will lead to improved efficiency and performance of the stimulating electrodes, reduced surgical trauma to the cochlea, and as a result, improved overall performance to the implant recipient. The positioning tool reported here consists of multiple fluidic chambers providing localized curvature control along the length of the attached silicon electrode array. The chambers consist of 200μm inner diameter PET (polyethylene therephthalate) tubes with 4μm wall thickness. The chambers are molded in a tapered helical configuration to correspond to the cochlear shape upon relaxation of the actuators. This ensures that the optimal electrode placement within the cochlea is retained after the positioning tool becomes dormant (for chronic implants). Actuation is achieved by injecting fluid into the PET chambers and regulating the fluidic pressure. The chambers are arranged in a stacked, overlapping design to provide fluid connectivity with the non-implantable pressure controller and allow for local curvature control of the device. The stacked tube configuration allows for localized curvature control of various areas along the length of the electrode and additional stiffening and actuating power towards the base. Curvature is affected along the entire length of a chamber and the result is cumulative in sections of multiple chambers. The actuating chambers are bonded to the back of a silicon electrode array

Computational Methods in Neuroengineering

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Update On Hearing Loss

Update on Hearing Loss encompasses both the theoretical background on the different forms of hearing loss and a detailed knowledge on state-of-the-art treatment for hearing loss, written for clinicians by specialists and researchers. Realizing the complexity of hearing loss has highlighted the importance of interdisciplinary research. Therefore, all the authors contributing to this book were chosen from many different specialties of medicine, including surgery, psychology, and neuroscience, and came from diverse areas of expertise, such as neurology, otolaryngology, psychiatry, and clinical and experimental audiology

Investigating the neural code for dynamic speech and the effect of signal degradation

It is common practice in psychophysical studies to investigate speech processing by manipulating or reducing spectral and temporal information in the input signal. Such investigations, along with the often surprising performance of modern cochlear implants, have highlighted the robustness of the auditory system to severe degradations and suggest that the ability to discriminate speech sounds is fundamentally limited by the complexity of the input signal. It is not clear, however, how and to what extent this is underpinned by neural processing mechanisms. This thesis examines the effect on the neural representation of reducing spectral and temporal information in the signal. A stimulus set from an existing psychophysical study was emulated, comprising a set of 16 vowel-consonant-vowel phoneme sequences (VCVs) each produced by multiple talkers, which were parametrically degraded using a noise-vocoder. Neuronal representations were simulated using a published computational model of the auditory nerve. Representations were also recorded in the inferior colliculus (IC) and auditory cortex (AC) of anaesthetised guinea pigs. Their discriminability was quantified using a novel neural classifier. Commensurate with investigations using simple stimuli, high rate envelope modulations in complex signals are represented in the auditory nerve and midbrain. It is demonstrated here that representations of these features are efficacious in a closed-set speech recognition task where appropriate decoding mechanisms are available, yet do not appear to be accessible perceptually. Optimal encoding windows for speech discrimination increase from of the order of 1 millisecond in the auditory nerve to 10s of milliseconds in the IC and the AC. Recent publications suggest that millisecond-precise neuronal activity is important for speech recognition. It is demonstrated here that the relevance of millisecond-precise responses in this context is highly dependent on the brain region, the nature of the speech recognition task and the complexity of the stimulus set

Electro-anatomical models of the cochlear implant

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 211-225).While cochlear implantation has become the standard care in treating patients with severe to profound sensorineural hearing loss, the variation in benefit (communicative ability) individual patients derive from implantation remains both large and, for the most part, unexplained. One explanation for this variation is the status of the implanted ear which, when examined histopathologically, also displays substantial variation due to both the pathogenesis of hearing loss (etiology, etc.) and pathological changes initiated by implantation. For instance, across-patient variation in electrode position and insertion depth is clearly present, as are differential amounts of residual spiral ganglion survival, fibrous tissue formation and electrode encapsulation, cochlear ossification, and idiosyncratic damage to adjacent cochlear structures. Because of the complex geometric electrical properties of the tissues found in the implanted ear, demonstrating the impact of pathological variability on neuronal excitation, and ultimately on behavioral performance, will likely require a detailed representation of the peripheral anatomy. Our approach has been to develop detailed, three-dimensional (3D) electro-anatomical models (EAMs) of the implanted ear capable of representing the aforementioned patient-specific types of pathological variation. In response to electric stimulation, these computational models predict an estimate of (1) the 3D electric field, (2) the cochleotopic pattern of neural activation, and (3) the electrically-evoked compound action potential (ECAP) recorded from intracochlear electrodes. This thesis focuses on three aims. First, two patient-specific EAMs are formulated from hundreds of digital images of the histologically-sectioned temporal bones of two patients, attempting to incorporate the detailed pathology of each. Second, model predictions are compared to relevant reports from the literature, data collected from a cohort of implanted research subjects, and, most importantly, to archival data collected during life from the same two patients used to derive our psychophysical threshold measures, and ECAP recordings) collectively show a promising correspondence between model-predicted and empirically-measured data. Third, by making incremental adjustments to the anatomical representation in the model, the impact of individual attributes are investigated, mechanisms that may degrade benefit suggested, and potential interventions explored.by Darren M. Whiten.Ph.D