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Magnetic stimulation of peripheral nerves is evoked by electric field gradients caused by high-intensity, pulsed magnetic fields created from a coil. Currents required for stimulation are very high, therefore devices are large, expensive, and often too complex for many applications like rehabilitation therapy. For repetitive stimulation, coil heating due to power loss poses a further limitation. The geometry of the magnetic coil determines field depth and focality, making it the most important factor that determines the current required for neuronal excitation. However, the comparison between different coil geometries is difficult and depends on the specific application. Especially the distance between nerve and coil plays a crucial role. In this investigation, the electric field distribution of 14 different coil geometries was calculated for a typical peripheral nerve stimulation with a 27 mm distance between axon and coil. Coil parameters like field strength and focality were determined with electromagnetic field simulations. In a second analysis, the activating function along the axon was calculated, which quantifies the efficiency of neuronal stimulation. Moreover, coil designs were evaluated concerning power efficacy based on ohmic losses. Our results indicate that power efficacy of magnetic neurostimulation can be improved significantly by up to 40% with optimized coil designs.

In this work, we present a simulation environment that makes it possible to determine threshold currents for magnetic peripheral nerve stimulation. Using the phrenic nerve as an example, we show that coil currents as low as 600 A are sufficient to trigger an action potential. These findings may help to advance research into artificial respiration using magnetic stimulation.

After hearing loss retrograde degeneration of spiral ganglion neurons (SGNs) has been described. Studies modeling the effects of degeneration mostly omitted peripheral processes (dendrites). Recent experimental observations indicated that degenerating SGNs manifested also a reduced diameter of their dendrites. We simulated populations of 400 SGNs inside a high resolution cochlear model with a cochlear implant, based on μCT scans of a human temporal bone. Cochlear implant stimuli were delivered as biphasic pulses in a monopolar configuration. Three SGN situations were simulated, based on our previous measurements of human SGN dendrites: (A) SGNs with intact dendrites (before degeneration), (B) degenerating SGNs, dendrites with a smaller diameter but original length, (C) degenerating SGNs, dendrites omitted. SGN fibers were mapped to characteristic frequency, and place pitch was estimated from excitation profiles. Results from degenerating SGNs (B, C) were similar. Most action potentials were initiated in the somatic area for all cases (A, B, C), except for areas near stimulating electrodes in the apex with intact SGNs (A), where action potentials were initiated in the distal dendrite. In most cases, degenerating SGNs had lower thresholds than intact SGNs (A) (down to –2 dB). Excitation profiles showed increased ectopic activation, i.e., activation of unintended neuronal regions, as well as similar neuronal regions excited by different apical electrodes, for degenerating SGNs (B, C). The estimated pitch showed cases of pitch reversals in apical electrodes for intact SGNs (A), as well as mostly identical pitches evoked by the four most apical electrodes for degenerating SGNs (B, C). In conclusion, neuronal excitation profiles to electrical stimulation exhibited similar traits in both ways of modeling SGN degeneration. Models showed degeneration of dendrites caused increased ectopic activation, as well as similar excitation profiles and pitch evoked by different apical electrodes. Therefore, insertion of electrodes beyond approximately 450° may not provide any benefit if SGN dendrites are degenerated.

In asymmetric treatment of hearing loss, processing latencies of the modalities typically differ. This often alters the reference interaural time difference (ITD) (i.e., the ITD at 0° azimuth) by several milliseconds. Such changes in reference ITD have shown to influence sound source localization in bimodal listeners provided with a hearing aid (HA) in one and a cochlear implant (CI) in the contralateral ear. In this study, the effect of changes in reference ITD on speech understanding, especially spatial release from masking (SRM) in normal-hearing subjects was explored. Speech reception thresholds (SRT) were measured in ten normal-hearing subjects for reference ITDs of 0, 1.75, 3.5, 5.25 and 7 ms with spatially collocated (S0N0) and spatially separated (S0N90) sound sources. Further, the cues for separation of target and masker were manipulated to measure the effect of a reference ITD on unmasking by A) ITDs and interaural level differences (ILDs), B) ITDs only and C) ILDs only. A blind equalization-cancellation (EC) model was applied to simulate all measured conditions. SRM decreased significantly in conditions A) and B) when the reference ITD was increased: In condition A) from 8.8 dB SNR on average at 0 ms reference ITD to 4.6 dB at 7 ms, in condition B) from 5.5 dB to 1.1 dB. In condition C) no significant effect was found. These results were accurately predicted by the applied EC-model. The outcomes show that interaural processing latency differences should be considered in asymmetric treatment of hearing loss.

Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli – an exploding and a burning box – interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern – a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by –1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.

Subjects utilizing a cochlear implant (CI) in one ear and a hearing aid (HA) on the contralateral ear suffer from mismatches in stimulation timing due to different processing latencies of both devices. This device delay mismatch leads to a temporal mismatch in auditory nerve stimulation. Compensating for this auditory nerve stimulation mismatch by compensating for the device delay mismatch can significantly improve sound source localization accuracy. One CI manufacturer has already implemented the possibility of mismatch compensation in its current fitting software. This study investigated if this fitting parameter can be readily used in clinical settings and determined the effects of familiarization to a compensated device delay mismatch over a period of 3–4 weeks. Sound localization accuracy and speech understanding in noise were measured in eleven bimodal CI/HA users, with and without a compensation of the device delay mismatch. The results showed that sound localization bias improved to 0°, implying that the localization bias towards the CI was eliminated when the device delay mismatch was compensated. The RMS error was improved by 18% with this improvement not reaching statistical significance. The effects were acute and did not further improve after 3 weeks of familiarization. For the speech tests, spatial release from masking did not improve with a compensated mismatch. The results show that this fitting parameter can be readily used by clinicians to improve sound localization ability in bimodal users. Further, our findings suggest that subjects with poor sound localization ability benefit the most from the device delay mismatch compensation.

Die akustische Analyse von Schnarchgerauschen ist eine nichtinvasive Methode fur die Diagnose von Entstehungsmechanismen des Schnarchens, die wahrend des naturlichen Schlafs durchgefuhrt werden kann. Ziel der Arbeit ist die Entwicklung und Bewertung von Klassifikationsschemata fur Schnarchgerausche, die eine moglichst aussagekraftige Diagnoseunterstutzung ermoglichen. Basierend auf 2 annotierten Schnarchgerauschdatenbanken mit unterschiedlicher Klassifikation (s-VOTE – 4 Klassen versus ACLTE – 5 Klassen) wurden identisch aufgebaute maschinelle Klassifikationssysteme trainiert. Der Merkmalsextraktor openSMILE wurde in Kombination mit einer linearen Support-Vektor-Maschine zur Klassifikation eingesetzt. Mit einem ungewichteten Average Recall (UAR) von 55,4 % fur das s‑VOTE-Modell und 49,1 % fur das ACLTE liegen die Ergebnisse auf ahnlichem Niveau. In beiden Modellen gelingt die beste Differenzierung fur Epiglottisschnarchen, wahrend velares und oropharyngeales Schnarchen haufiger verwechselt werden. Automatisierte akustische Verfahren konnen bei der Diagnose von Schlafatmungsstorungen unterstutzen. Einschrankungen in der Erkennungsleistung sind u. a. durch die begrenzte Grose der Trainingsdatensatze bedingt.

We estimated the electrically-evoked auditory brainstem response thresholds (eABR THRs) in response to multi-pulses with high burst rate of 10,000 pulses-per-second (pps). Growth functions of wave eV amplitudes, root mean square (RMS) values, peak of phase-locking value (PLV), and the lowest valid data point (LVDP) were calculated in 1-, 2-, 4-, 8-, and 16-pulses conditions. The growth functions were then fitted and extrapolated with linear and exponential functions to find eABR THRs. The estimated THRs were compared to psychophysical THRs determined for multi-pulse conditions as well as to the clinical THRs measured behaviorally at the rate of 1,000 pps. The growth functions of features showed shallower growth slopes when the number of pulses increased. eABR THRs estimated in 4-, 8-, and 16-pulses conditions were closer to the clinical THRs, when compared to 1- and 2-pulses conditions. However, the smallest difference between estimated eABR THRs and clinical THRs was not always achieved from the same number of pulses. The smallest absolute difference of 30.3 μA was found for the linear fittings on growth functions of eABR RMS values in 4-pulses condition. Pearson’s correlation coefficients (PCCs) between eABR THRs and psychophysical THRs were significant and relatively large in all but 16-pulses conditions. The PCCs between eABR THRs and clinical THRs, however, were smaller and in less cases significant. Results of this study showed that eABRs to multi-pulse stimulation could, to some extent, represent clinical stimulation paradigms, and thus in comparison to single pulses, could estimate clinical THRs with smaller errors.

We estimated the electrically-evoked auditory brainstem response thresholds (eABR THRs) in response to multi-pulses with high burst rate of 10,000 pulses-per-second (pps). Growth functions of wave eV amplitudes, root mean square (RMS) values, peak of phase-locking value (PLV), and the lowest valid data point (LVDP) were calculated in 1-, 2-, 4-, 8-, and 16-pulses conditions. The growth functions were then fitted and extrapolated with linear and exponential functions to find eABR THRs. The estimated THRs were compared to psychophysical THRs determined for multi-pulse conditions as well as to the clinical THRs measured behaviorally at the rate of 1,000 pps. The growth functions of features showed shallower growth slopes when the number of pulses increased. eABR THRs estimated in 4-, 8-, and 16-pulses conditions were closer to the clinical THRs, when compared to 1- and 2-pulses conditions. However, the smallest difference between estimated eABR THRs and clinical THRs was not always achieved from the same number of pulses. The smallest absolute difference of 30.3 μA was found for the linear fittings on growth functions of eABR RMS values in 4-pulses condition. Pearson’s correlation coefficients (PCCs) between eABR THRs and psychophysical THRs were significant and relatively large in all but 16-pulses conditions. The PCCs between eABR THRs and clinical THRs, however, were smaller and in less cases significant. Results of this study showed that eABRs to multi-pulse stimulation could, to some extent, represent clinical stimulation paradigms, and thus in comparison to single pulses, could estimate clinical THRs with smaller errors.

Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner. However, such image pairs are often not available in clinical practice. In this paper, we address super-resolution problem in a real-world scenario using unpaired data and synthesize linearly \textbf{eight times} higher resolved Micro-CT images of temporal bone structure, which is embedded in the inner ear. We explore cycle-consistency generative adversarial networks for super-resolution task and equip the translation approach with Bayesian inference. We further introduce \emph{Hu Moment distance} the evaluation metric to quantify the shape of the temporal bone. We evaluate our method on a public inner ear CT dataset and have seen both visual and quantitative improvement over state-of-the-art deep-learning-based methods. In addition, we perform a multi-rater visual evaluation experiment and find that trained experts consistently rate the proposed method the highest quality scores among all methods. Furthermore, we are able to quantify uncertainty in the unpaired translation task and the uncertainty map can provide structural information of the temporal bone., final version in ISBI 2021

We present a setup for measuring in-plane motion of MEMS with picometer resolution. The system uses stroboscopic video microscopy to capture image sequences from periodic motions that are analyzed with gradient-based sub-pixel algorithms.The performance of the setup was demonstrated by measuring the frequency response along the length of an atomic-force cantilever and a dense mapping of the strain field on the surface of a flexoelectric MEMS device.Results demonstrate the superiority of our system compared to previous systems: 50 pm noise floor in single-shot experiments and frequency range up to 2 MHz.

In the past three decades, snoring (affecting more than 30 % adults of the UK population) has been increasingly studied in the transdisciplinary research community involving medicine and engineering. Early work demonstrated that, the snore sound can carry important information about the status of the upper airway, which facilitates the development of non-invasive acoustic based approaches for diagnosing and screening of obstructive sleep apnoea and other sleep disorders. Nonetheless, there are more demands from clinical practice on finding methods to localise the snore sound's excitation rather than only detecting sleep disorders. In order to further the relevant studies and attract more attention, we provide a comprehensive review on the state-of-the-art techniques from machine learning to automatically classify snore sounds. First, we introduce the background and definition of the problem. Second, we illustrate the current work in detail and explain potential applications. Finally, we discuss the limitations and challenges in the snore sound classification task. Overall, our review provides a comprehensive guidance for researchers to contribute to this area.

Computational human head models have been used in studies of brain stimulation. These models have been able to provide useful information that can’t be acquired or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. We hereby combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 2000 neural fibres from diffusion-weighted magnetic resonance scans and extracted the information on electrical potential from the FE ECT model of the same head. Two different electrode placements and three different white matter conductivity settings were simulated and compared. We calculated the electric field and second spatial derivatives of the electrical potential along the fibre direction, which describes the activating function for homogenous axons, and investigated sensitive regions of white matter activation. Models with anisotropic white matter conductivity yielded the most distinctive electric field and activating function distribution. Activation was most likely to appear in regions between the electrodes where the electric potential gradient is most pronounced.

Existing computational studies of cochlear implants have demonstrated that the structural detail of threedimensional (3D) cochlear models exerts influence on the current spread within the cochlea. Nevertheless, the significance of including the microstructures inside the modiolar bone in a cochlear model is still unclear in the literature. We employed two different multi-compartment neuron models to simulate auditory nerve fibres, and compared response characteristics of the fibre population between a detailed and a simplified 3D cochlear model. Results showed that although the prediction of firing is dependent on the details of the neuron model, the responses of the fibre population to the electrical stimulus, especially the location of the initiation of action potential, varied between the detailed and the simplified models. Therefore, the inclusion of the modiolar microstructures in a cochlear model may be necessary for fully understanding the firing of auditory nerve fibres.

We investigated the effects of electric multi-pulse stimulation on electrically evoked auditory brainstem responses (eABRs). Multi-pulses with a high burst rate of 10,000 pps were assembled from pulses of 45-μs phase duration. Conditions of 1, 2, 4, 8, and 16 pulses were investigated. Psychophysical thresholds (THRs) and most comfortable levels (MCLs) in multi-pulse conditions were measured. Psychophysical temporal integration functions (slopes of THRs/MCLs as a function of number of pulses) were −1.30 and −0.93 dB/doubling of the number of pulses, which correspond to the doubling of pulse duration. A total of 15 eABR conditions with different numbers of pulses and amplitudes were measured. The morphology of eABRs to multi-pulse stimuli did not differ from those to conventional single pulses. eABR wave eV amplitudes and latencies were analyzed extensively. At a fixed stimulation amplitude, an increasing number of pulses caused increasing wave eV amplitudes up to a certain, subject-dependent number of pulses. Then, amplitudes either saturated or even decreased. This contradicted the conventional amplitude growth functions and also contradicted psychophysical results. We showed that destructive interference could be a possible reason for such a finding, where peaks and troughs of responses to the first pulses were suppressed by those of successive pulses in the train. This study provides data on psychophysical THRs and MCLs and corresponding eABR responses for stimulation with single-pulse and multi-pulse stimuli with increasing duration. Therefore, it provides insights how pulse trains integrate at the level of the brainstem.

Snore sound (SnS) classification can support a targeted surgical approach to sleep related breathing disorders. Using machine listening methods, we aim to find the location of obstruction and vibration within a subject's upper airway. Wavelet features have been demonstrated to be efficient in the recognition of SnSs in previous studies. In this work, we use a bag-of-audio-words approach to enhance the low-level wavelet features extracted from SnS data. A Naive Bayes model was selected as the classifier based on its superiority in initial experiments. We use SnS data collected from 219 independent subjects under drug-induced sleep endoscopy performed at three medical centres. The unweighted average recall achieved by our proposed method is 69.4%, which significantly ([Formula: see text] one-tailed z-test) outperforms the official baseline (58.5%), and beats the winner (64.2%) of the INTERSPEECH COMPARE Challenge 2017 Snoring sub-challenge. In addition, the conventionally used features like formants, mel-scale frequency cepstral coefficients, subband energy ratios, spectral frequency features, and the features extracted by the OPENSMILE toolkit are compared with our proposed feature set. The experimental results demonstrate the effectiveness of the proposed method in SnS classification.

Objective Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Methods Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. Results An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. Conclusion A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters.

Background: Many detailed features of the cochlear anatomy have not been included in existing 3D cochlear models, including the microstructures inside the modiolar bone, which in turn determines the path of auditory nerve fibers (ANFs).Method: We captured the intricate modiolar microstructures in a 3D human cochlea model reconstructed from μCT scans. A new algorithm was developed to reconstruct ANFs running through the microstructures within the model. Using the finite element method, we calculated the electrical potential as well as its first and second spatial derivatives along each ANF elicited by the cochlear implant electrodes. Simulation results of electrical potential was validated against intracochlear potential measurements. Comparison was then made with a simplified model without the microstructures within the cochlea.Results: When the stimulus was delivered from an electrode located deeper in the apex, the extent of the auditory nerve influenced by a higher electric potential grew larger; at the same time, the maximal potential value at the auditory nerve also became larger. The electric potential decayed at a faster rate toward the base of the cochlea than toward the apex. Compared to the cochlear model incorporating the modiolar microstructures, the simplified version resulted in relatively small differences in electric potential. However, in terms of the first and second derivatives of electric potential along the fibers, which are relevant for the initiation of action potentials, the two models exhibited large differences: maxima in both derivatives with the detailed model were larger by a factor of 1.5 (first derivative) and 2 (second derivative) in the exemplary fibers. More importantly, these maxima occurred at different locations, and opposite signs were found for the values of second derivatives between the two models at parts along the fibers. Hence, while one model predicts depolarization and spike initiation at a given location, the other may instead predict a hyperpolarization.Conclusions: Although a cochlear model with fewer details seems sufficient for analysing the current spread in the cochlear ducts, a detailed-segmented cochlear model is required for the reconstruction of ANF trajectories through the modiolus, as well as the prediction of firing thresholds and spike initiation sites.

Background: Multi-compartment cable models of auditory nerve fibers have been developed to assist in the improvement of cochlear implants. With the advancement of computational technology and the results obtained from in vivo and in vitro experiments, these models have evolved to incorporate a considerable degree of morphological and physiological details. They have also been combined with three-dimensional volume conduction models of the cochlea to simulate neural responses to electrical stimulation. However, no specific rules have been provided on choosing the appropriate cable model, and most models adopted in recent studies were chosen without a specific reason or by inheritance.Methods: Three of the most cited biophysical multi-compartment cable models of the human auditory nerve, i.e., Rattay et al. (2001b), Briaire and Frijns (2005), and Smit et al. (2010), were implemented in this study. Several properties of single fibers were compared among the three models, including threshold, conduction velocity, action potential shape, latency, refractory properties, as well as stochastic and temporal behaviors. Experimental results regarding these properties were also included as a reference for comparison.Results: For monophasic single-pulse stimulation, the ratio of anodic vs. cathodic thresholds in all models was within the experimental range despite a much larger ratio in the model by Briaire and Frijns. For biphasic pulse-train stimulation, thresholds as a function of both pulse rate and pulse duration differed between the models, but none matched the experimental observations even coarsely. Similarly, for all other properties including the conduction velocity, action potential shape, and latency, the models presented different outcomes and not all of them fell within the range observed in experiments.Conclusions: While all three models presented similar values in certain single fiber properties to those obtained in experiments, none matched all experimental observations satisfactorily. In particular, the adaptation and temporal integration behaviors were completely missing in all models. Further extensions and analyses are required to explain and simulate realistic auditory nerve fiber responses to electrical stimulation.

Background: Multi-compartment cable models of auditory nerve fibers have been developed to assist in the improvement of cochlear implants. With the advancement of computational technology and the results obtained from in vivo and in vitro experiments, these models have evolved to incorporate a considerable degree of morphological and physiological details. They have also been combined with three-dimensional volume conduction models of the cochlea to simulate neural responses to electrical stimulation. However, no specific rules have been provided on choosing the appropriate cable model, and most models adopted in recent studies were chosen without a specific reason or by inheritance. Methods: Three of the most cited biophysical multi-compartment cable models of the human auditory nerve, i.e., Rattay et al. (2001b), Briaire and Frijns (2005), and Smit et al. (2010), were implemented in this study. Several properties of single fibers were compared among the three models, including threshold, conduction velocity, action potential shape, latency, refractory properties, as well as stochastic and temporal behaviors. Experimental results regarding these properties were also included as a reference for comparison. Results: For monophasic single-pulse stimulation, the ratio of anodic vs. cathodic thresholds in all models was within the experimental range despite a much larger ratio in the model by Briaire and Frijns. For biphasic pulse-train stimulation, thresholds as a function of both pulse rate and pulse duration differed between the models, but none matched the experimental observations even coarsely. Similarly, for all other properties including the conduction velocity, action potential shape, and latency, the models presented different outcomes and not all of them fell within the range observed in experiments. Conclusions: While all three models presented similar values in certain single fiber properties to those obtained in experiments, none matched all experimental observations satisfactorily. In particular, the adaptation and temporal integration behaviors were completely missing in all models. Further extensions and analyses are required to explain and simulate realistic auditory nerve fiber responses to electrical stimulation.

Objective- The distinction of snoring and loud breathing is often subjective and lies in the ear of the beholder. The aim of this study is to identify and assess acoustic features with a high suitability to distinguish these two classes of sound, in order to facilitate an objective definition of snoring based on acoustic parameters. Methods- A corpus of snore and breath sounds from 23 subjects has been used that were classified by 25 human raters. Using the openSMILE feature extractor, 6 373 acoustic features have been evaluated for their selectivity comparing SVM classification, logistic regression, and the recall of each single feature. Results- Most selective single features were several statistical functionals of the first and second mel frequency spectrum-generated perceptual linear predictive (PLP) cepstral coefficient with an unweighted average recall (UAR) of up to 93.8%. The best performing feature sets were low level descriptors (LLDs), derivatives and statistical functionals based on fast Fourier transformation (FFT), with a UAR of 93.0%, and on the summed mel frequency spectrum-generated PLP cepstral coefficients, with a UAR of 92.2% using SVM classification. Compared to SVM classification, logistic regression did not show considerable differences in classification performance. Conclusion- It could be shown that snoring and loud breathing can be distinguished by robust acoustic features. The findings might serve as a guidance to find a consensus for an objective definition of snoring compared to loud breathing.

Computational human head models have been used in electrophysiological studies, and they have been able to provide useful information that is unable or difficult to acquire from experimental or imaging studies. However, most of these models are purely volume conductor models that overlooked the electric excitability of axons in the white matter of the brain. This study combined a finite element (FE) model of electroconvulsive therapy (ECT) with a whole-brain tractography analysis as well as the cable theory of neuronal excitation. We have reconstructed a whole-brain tractogram with 500 neural fibres from the diffusion-weighted magnetic resonance scans, and extracted the information on electrical potential from the FE ECT model of the same head. We then calculated the first and second spatial derivatives of the electrical potential, which describes the activating function for homogenous axons and investigated sensitive regions of white matter activation.

Three-dimensional (3D) computational models of the inner ear have been utilised to assist in investigating the factors that influence cochlear implant (CI) outcomes. A volume conductor cochlear model with an implanted electrode array was reconstructed from X-ray microtomography $(\mu$ CT) scans of a cadaveric human temporal bone. To mimic an in-vivo setting, the cochlea was embedded in a head model. The finite element (FE) method was used to analyse the electrical potential $\varphi$ in the cochlear nerve as a result of CI stimulation. In order to study the influence of electrode array placement on the current spread within the cochlea and the modiolus, computer simulations with six electrode array placements were conducted. $\varphi$ was evaluated at the tip of nerve fibres reconstructed within the cochlear nerve so as to predict the stimulation of a neuron population. It was found in most cases that a medial electrode array placement produced a narrower $\varphi$ peak at the fibre tip than a lateral one, although the differences were small.

In cochlear implants, severe limitations arise from electrical crosstalk between channels. Therefore, the current trend in cochlear implants is to increase stimulation rates to encode signals with higher temporal precision. However, the fundamental question: "What is the limit of temporal precision due to inherent neuronal dynamics of the stimulated neurons?" has not yet been resolved. In this study we have developed a double-pulse method and, for the first time, reversed stimulus polarity systematically between consecutive pulses to elucidate subthreshold-induced temporal interaction effects. This method allowed us to determine the time-course of subthreshold temporal interaction in human subjects which identifies the limits of encoded temporal precision. Our results show significant temporal interaction up to 600 μs inter-pulse interval. In all the cases tested we saw a facilitation effect on threshold. Interaction effects at a 20% below threshold pre-conditioning stimulation showed up to 38% ± 6% threshold reduction. These results imply that there is significant temporal interaction between two subsequent pulses. This interaction diminishes the precision of amplitude coding. We predict interaction effects on temporal precision and channel interaction. For (interleaved) stimulation with short inter-pulse intervals it is interesting to consider our interaction results; and it may become important to consider them for future coding strategies where high temporal precision is required. In an increasing group of binaural implanted patients this will be the case when interaural time differences are encoded with μs precision.

Objective: Obstructive sleep apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. Methods: Snore sounds from 40 male patients have been recorded during drug-induced sleep endoscopy, and categorized by Ear, Nose & Throat (ENT) experts. Crest Factor, fundamental frequency, spectral frequency features, subband energy ratio, mel-scale frequency cepstral coefficients, empirical mode decomposition-based features, and wavelet energy features have been extracted and fed into several classifiers. Using the ReliefF algorithm, features have been ranked and the selected feature subsets have been tested with the same classifiers. Results: A fusion of all features after a ReliefF feature selection step in combination with a random forests classifier showed the best classification results of 78% unweighted average recall by subject independent validation. Conclusion: Multifeature analysis is a promising means to help identify the anatomical mechanisms of snore sound generation in individual subjects. Significance: This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.

In certain conditions, especially with diotic headphone presentation, hearing sensations are located inside the head. Dichotic headphone presentation can result in lateralization: delaying one of the headphone input signals or reducing its amplitude typically pushes the hearing sensation inside the head towards the contralateral ear. Systematic connections between physical stimulus parameters and hearing-sensation positions provide insight into auditory-localization mechanisms and are helpful in designing and evaluating models. However, typical paradigms of addressing lateral displacement, magnitude estimation or pointing, may suffer from response biases. This study aims at evaluating the suitability of a two-alternative forced choice paradigm: eight normal-hearing subjects were asked to indicate, by pushing one of two buttons, whether the hearing sensation associated with a pure interaural phase or intensity difference occurred “left or right”, without any reference or further instructions. Posing a notably simple task without internal mapping, this procedure appears advantageous regarding response biases. The results indicate a high intra-individual reproducibility and plausible inter-individual agreement. None of the participants encountered or reported difficulties using the inherently assumed “internal center” in their decision process. The data suggests the existence of an inter-individually similar decision criterion. Descriptively speaking, the results support the existence of a similar lateralization midpoint.In certain conditions, especially with diotic headphone presentation, hearing sensations are located inside the head. Dichotic headphone presentation can result in lateralization: delaying one of the headphone input signals or reducing its amplitude typically pushes the hearing sensation inside the head towards the contralateral ear. Systematic connections between physical stimulus parameters and hearing-sensation positions provide insight into auditory-localization mechanisms and are helpful in designing and evaluating models. However, typical paradigms of addressing lateral displacement, magnitude estimation or pointing, may suffer from response biases. This study aims at evaluating the suitability of a two-alternative forced choice paradigm: eight normal-hearing subjects were asked to indicate, by pushing one of two buttons, whether the hearing sensation associated with a pure interaural phase or intensity difference occurred “left or right”, without any reference or further instructions. Posing a nota...

Globular bushy cells (GBCs) located in the ventral cochlear nucleus are an essential part of the sound localization pathway in the mammalian auditory system. They receive inputs directly from the auditory nerve and are particularly sensitive to temporal cues due to their synaptic and membrane specializations. GBCs act as coincidence detectors for incoming spikes through large synapses—endbulbs of Held—which connect to their soma. Since endbulbs of Held are an integral part of the auditory information conveying and processing pathway, they were extensively studied. Virtually all in vitro studies showed large synaptic depression, but on the other hand a few in vivo studies showed relatively small depression. It is also still not well understood how synaptic properties functionally influence firing properties of GBCs. Here we show how different levels of synaptic depression shape firing properties of GBCs in in vivo-like conditions using computer simulations. We analyzed how an interplay of synaptic depression (0–70%) and the number of auditory nerve fiber inputs (10–70) contributes to the variability of the experimental data from previous studies. We predict that the majority of synapses of GBCs with high characteristic frequencies (CF > 500 Hz) have a rate dependent depression of less than 20%. GBCs with lower CF (

This review evaluates the potential of optogenetic methods for the stimulation of the auditory nerve and assesses the feasability of optogenetic cochlear implants (CIs). It provides an overview of all critical steps like opsin targeting strategies, how opsins work, how their function can be modeled and included in neuronal models and the properties of light sources available for optical stimulation. From these foundations, quantitative estimates for the number of independent stimulation channels and the temporal precision of optogenetic stimulation of the auditory nerve are derived and compared with state-of-the-art electrical CIs. We conclude that optogenetic CIs have the potential to increase the number of independent stimulation channels by up to one order of magnitude to about 100, but only if light sources are able to deliver confined illumination patterns independently and parallelly. Already now, opsin variants like ChETA and Chronos enable driving of the auditory nerve up to rates of 200 spikes/s, close to the physiological value of their maximum sustained firing rate. Apart from requiring 10 times more energy than electrical stimulation, optical CIs still face major hurdles concerning the safety of gene transfection and optrode array implantation, for example, before becoming an option to replace electrical CIs.

The present study evaluated whether the poorer baseline performance of cochlear implant (CI) users or the technical and/or physiological properties of CI stimulation are responsible for the absence of masking release.This study measured speech reception thresholds (SRTs) in continuous and modulated noise as a function of signal to noise ratio (SNR).A total of 24 subjects participated: 12 normal-hearing (NH) listeners and 12 subjects provided with recent MED-EL CI systems.The mean SRT of CI users in continuous noise was -3.0 ± 1.5 dB SNR (mean ± SEM), while the normal-hearing group reached -5.9 ± 0.8 dB SNR. In modulated noise, the difference across groups increased considerably. For CI users, the mean SRT worsened to -1.4 ± 2.3 dB SNR, while it improved for normal-hearing listeners to -18.9 ± 3.8 dB SNR.The detrimental effect of fluctuating maskers on SRTs in CI users shown by prior studies was confirmed by the current study. Concluding, the absence of masking release is mainly caused by the technical and/or physiological properties of CI stimulation, not just the poorer baseline performance of many CI users compared to normal-hearing subjects. Speech understanding in modulated noise was more robust in CI users who had a relatively large electrical dynamic range.

Biomedical optics express 9(11), 5330 - 5339 (2018). doi:10.1364/BOE.9.005330, Propagation-based phase-contrast computed tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high contrast between materials with similar attenuation properties. However, one of the most-widely used phase-retrieval algorithms imposes a homogeneity assumption onto the sample, which leads to artifacts for numerous applications where this assumption is violated. Prominent examples are biological samples with highly-absorbing implants. Using synchrotron radiation, we demonstrate by the example of a guinea pig inner ear with a cochlear implant electrode, how a recently developed model-based iterative algorithm for propagation-based phase-contrast computed tomography yields distinct benefits for such a task. We find that the model-based approach improves the overall image quality, removes the detrimental influence of the implant and accurately visualizes the cochlea., Published by OSA, Washington, DC

In certain conditions, especially with diotic headphone presentation, hearing sensations are located inside the head. Dichotic headphone presentation can result in lateralization: delaying one of the headphone input signals or reducing its amplitude typically pushes the hearing sensation inside the head towards the contralateral ear. Systematic connections between physical stimulus parameters and hearing-sensation properties provide insight into auditory-localization mechanisms and are helpful in designing and evaluating models thereof. While various studies on the lateral displacement of hearing sensations exist, fewer data is available on the respective shape or spatial extent. Based on observations from a pure-tone lateralization experiment, this study aims at taking a first step towards quantifying the typical shape of the corresponding hearing sensations: in two separate experiments, twelve normal-hearing subjects reported a) the overall spatial extent of the hearing sensation or, if applicable, of all simultaneously occurring sensations and b) the number of simultaneously occurring, spatially separated hearing sensations. The data suggests, in line with earlier studies, considerable differences between the results for pure interaural phase and amplitude differences, respectively. Based on the results, an initial empirical description of the stimulus-dependent hearing sensations in pure-tone lateralization summarizes the observations, especially regarding location and spatial shape.In certain conditions, especially with diotic headphone presentation, hearing sensations are located inside the head. Dichotic headphone presentation can result in lateralization: delaying one of the headphone input signals or reducing its amplitude typically pushes the hearing sensation inside the head towards the contralateral ear. Systematic connections between physical stimulus parameters and hearing-sensation properties provide insight into auditory-localization mechanisms and are helpful in designing and evaluating models thereof. While various studies on the lateral displacement of hearing sensations exist, fewer data is available on the respective shape or spatial extent. Based on observations from a pure-tone lateralization experiment, this study aims at taking a first step towards quantifying the typical shape of the corresponding hearing sensations: in two separate experiments, twelve normal-hearing subjects reported a) the overall spatial extent of the hearing sensation or, if applicable, of a...

The auditory system of humans and other mammals is able to use interaural time and intensity differences as well as spectral cues to localize sound sources. One important cue for localizing low-frequency sound sources in the horizontal plane are interaural time differences (ITDs), which are first analyzed in the medial superior olive (MSO) in the brainstem. Results from electrophysiological and psychoacoustic studies suggest ITD encoding in the relative activities of neuronal populations in the two brain hemispheres. This contribution first explores the neuronal encoding of fine-structure ITDs using a physiologically motivated spiking neuronal-network model of the mammalian MSO. Results from this model confirm robust ITD encoding in the relative activity of the MSOs in both hemispheres. Based on the neuronal-network simulations, a simple probabilistic model of subsequent ITD processing is proposed. This simplified model connects the neuronal responses of the two hemispheres with different hearing sensatio...

Videomicroscopy with subpixel analysis is an excellent system for quantification of outer hair cell (OHC) movements. The resolution of a few nanometers is accurate enough to show induced differences of electromotility.Electromotility of OHCs is a voltage-dependent process resulting from a membrane protein named prestin. Voltage sensitivity is conferred to prestin by intracellular anions. Reduction of these anions reduces electromotility. Videomicroscopy and subpixel tracking combine video-based analysis with a resolution of few nanometers. The aim of this study was to show the feasibility of a system for quantification of OHC movements.Electromotility was investigated under normal and reduced intracellular chloride conditions. Cells were stimulated by the patch-clamp technique. Voltage steps were 500 ms long, ranging from -170 to +30 mV in 10 mV steps.As in previous studies our results show the following. The direction of OHC movement depends on the polarity of voltage steps, length changes are not equal for symmetrical voltage steps of opposite polarity, average shortening for a depolarizing step (-70 mV to +30 mV) is about 13 nm/mV. Hyperpolarization (-70 mV to -170 mV) on average evokes elongations of about 3 nm/mV. Half maximal chloride concentration reduces motility by 14%; half maximal electromotility is reached by a 94% reduction of chloride.

Several possibilities exist for quantifying speech intelligibility in noise, for example tests with single words or sentences. In the clinical routine, usually a single sentence test is used repeatedly, so that its vocabulary is often known to the patients. Thus, not only speech intelligibility but also long-term memory is tested. New speech material is then required to explicitly address intelligibility. With the aim of providing new and optimized material, in this pilot study, semantically, syntactically, and phonetically balanced words were taken from German sentence tests, based on databases for written and spoken German words. The resulting vocabulary consists of 54 words (nouns, adjectives, verbs, and numbers), for which word-reception thresholds were determined for ten normal-hearing subjects. With these thresholds, five-word lists of equal semantic context, equal word intelligibility, and with balanced phonetic cues were built. Non-acoustic advantages of an intelligibility test constructed this wa...

Our notion of how speech is processed is still very much dominated by von Helmholtz's theory of hearing. He deduced that the human inner ear decomposes the spectrum of sound signals. However, physiological recordings of auditory nerve fibers (ANF) showed that the rate-place code, which is thought to transmit spectral information to the brain, is at least complemented by a temporal code. In our paper we challenge the rate-place code using a complex but realistic scenario: speech in noise. We used a detailed model of human auditory processing that closely replicates key aspects of auditory nerve spike trains. We performed quantitative evaluations of coding strategies using standard automatic speech recognition (ASR) tools. Our test data was spoken letters of the whole English alphabet from a variety of speakers, with and without background noise. We evaluated a purely rate-place-based encoding strategy, a temporal strategy based on interspike intervals, and a combination thereof. The results suggest that as few as 4% of the total number of ANFs would be sufficient to code speech information in a rate-place fashion. Rate-place coding performed its best for speech in clean conditions at normal sound level, but broke down at higher-than-normal levels, and failed dramatically in noise at high levels. Low-spontaneous rate fibers improved the rate-place code, mainly for vowels and at higher-than-normal levels. At high speech levels, and in particular in the presence of background noise, combining rate-place coding with the temporal coding strategy greatly improved recognition accuracy. We therefore conclude that the human auditory system does not rely on a rate-place code alone but requires the abundance of fibers for precise temporal coding.

The mel-frequency cepstral coefficient (MFCC) or perceptual linear prediction (PLP) feature extraction typically used for automatic speech recognition (ASR) employ several principles which have known counterparts in the cochlea and auditory nerve: frequency decomposition, mel- or bark-warping of the frequency axis, and compression of amplitudes. It seems natural to ask if one can profitably employ a counterpart of the next physiological processing step, synaptic adaptation. We, therefore, incorporated a simplified model of short-term adaptation into MFCC feature extraction. We evaluated the resulting ASR performance on the AURORA 2 and AURORA 3 tasks, in comparison to ordinary MFCCs, MFCCs processed by RASTA, and MFCCs processed by cepstral mean subtraction (CMS), and both in comparison to and in combination with Wiener filtering. The results suggest that our approach offers a simple, causal robustness strategy which is competitive with RASTA, CMS, and Wiener filtering and performs well in combination with Wiener filtering. Compared to the structurally related RASTA, our adaptation model provides superior performance on AURORA 2 and, if Wiener filtering is used prior to both approaches, on AURORA 3 as well.

In this paper, we investigate computing air damping in a comb-drive resonator by numerically solving the three-dimensional (3-D) Stokes equation for the entire resonator using the FastStokes Solver. In addition, we used a recently developed computer microvision system to directly measure resonator frequency response. By comparing the measured results to those generated by one dimensional analytic models and by numerical solution of the 3-D Stokes' equation, we demonstrate that numerically solving the Stokes' equation is fast and also generates a model that matches quality the factor to within 10%. We also show that results based on one-dimensional (1-D) models mispredict quality factor by more than a factor of two. In addition, the detailed drag force distribution generated by the FastStokes solver is used to identify sources of errors in the 1-D models.

Dynamic material properties of the tectorial membrane (TM) have been measured at audio frequencies in TMs excised from the apical portions of mouse cochleae. We review, integrate, and interpret recent findings. The mechanical point impedance of the TM in the radial, longitudinal, and transverse directions is viscoelastic and has a frequency dependence of the form 1/(K(j2pif)(alpha)) for 10or=for=4000 Hz, where f is frequency, K is a constant, j=-1, and alpha approximately 0.66. Comparison with other connective tissues shows that the TM is a relatively lossy viscoelastic material. The median magnitudes of the point impedance at 10 Hz in the radial, longitudinal, and transverse directions are 4.6 x 10(-3) N.s/m, 1.8 x 10(-3) N.s/m, and 2.7 x 10(-3) N.s/m. Consistent with osmotic responses (Freeman et al., 2003), the TM point impedance is anisotropic - the TM is stiffer in the radial than in the longitudinal and transverse directions. The mechanical space constant of the TM is approximately 20 microm. Comparisons reveal that in the apical region of the mouse cochlea, the TM dynamic stiffness at 10 Hz is 10 times larger than the static stiffness of the aggregate hair cells in a mechanical space constant and roughly comparable to the stiffness of the basilar membrane. We conclude that the TM provides a mechanical load on the basilar membrane and that the lability of the TM to changes in endolymph composition may well be reflected in changes in basilar membrane motion.

Models are valuable tools to assess how deeply we understand complex systems: only if we are able to replicate the output of a system based on the function of its subcomponents can we assume that we have probably grasped its principles of operation. On the other hand, discrepancies between model results and measurements reveal gaps in our current knowledge, which can in turn be targeted by matched experiments. Models of the auditory periphery have improved greatly during the last decades, and account for many phenomena observed in experiments. While the cochlea is only partly accessible in experiments, models can extrapolate its behavior without gap from base to apex and with arbitrary input signals. With models we can for example evaluate speech coding with large speech databases, which is not possible experimentally, and models have been tuned to replicate features of the human hearing organ, for which practically no invasive electrophysiological measurements are available. Auditory models have become instrumental in evaluating models of neuronal sound processing in the auditory brainstem and even at higher levels, where they are used to provide realistic input, and finally, models can be used to illustrate how such a complicated system as the inner ear works by visualizing its responses. The big advantage there is that intermediate steps in various domains (mechanical, electrical, and chemical) are available, such that a consistent picture of the evolvement of its output can be drawn. However, it must be kept in mind that no model is able to replicate all physiological characteristics (yet) and therefore it is critical to choose the most appropriate model—or models—for every research question. To facilitate this task, this paper not only reviews three recent auditory models, it also introduces a framework that allows researchers to easily switch between models. It also provides uniform evaluation and visualization scripts, which allow for direct comparisons between models.

High-frequency resolution is one of the salient features of peripheral sound processing in the mammalian cochlea. The sensitivity originates in the active amplification of the travelling wave on the basilar membrane by the outer hair cells (OHCs), where electrically induced mechanical action of the OHC on a cycle-by-cycle basis is believed to be the crucial component. However, it is still unclear if this electromechanical action is sufficiently fast and can produce enough force to enhance mechanical tuning up to the highest frequencies perceived by mammals. Here we show that isolated OHCs in the microchamber configuration are able to overcome fluid forces with almost constant displacement amplitude and phase up to frequencies well above their place-frequency on the basilar membrane. The high-frequency limit of the electromotility, defined as the frequency at which the amplitude drops by 3 dB from its asymptotic low-frequency value, is inversely dependent on cell length. The frequency limit is at least 79 kHz. For frequencies up to 100 kHz, the electromotile response was specified by an overdamped ( Q = 0.42) second-order resonant system. This finding suggests that the limiting factor for frequencies up to 100 kHz is not the speed of the motor but damping and inertia. The isometric force produced by the OHC was constant at least up to 50 kHz, with amplitudes as high as 53 pN/mV being observed. We conclude that the electromechanical transduction process of OHCs possesses the necessary high-frequency properties to enable amplification of the travelling wave over the entire hearing range.