The effects of estimation accuracy, estimation approach, and number of selected channels using formant-priority channel selection for an "n-of-m" sound processing strategy for cochlear implants.

Previously, selection of l channels was prioritized according to formant frequency locations in an l-of-n-of-m-based signal processing strategy to provide important voicing information independent of listening environments for cochlear implant (CI) users. In this study, ideal, or ground truth, formants were incorporated into the selection stage to determine the effect of accuracy on (1) subjective speech intelligibility, (2) objective channel selection patterns, and (3) objective stimulation patterns (current). An average +11% improvement (p < 0.05) was observed across six CI users in quiet, but not for noise or reverberation conditions. Analogous increases in channel selection and current for the upper range of F1 and a decrease across mid-frequencies with higher corresponding current, were both observed at the expense of noise-dominant channels. Objective channel selection patterns were analyzed a second time to determine the effects of estimation approach and number of selected channels (n). A significant effect of estimation approach was only observed in the noise and reverberation condition with minor differences in channel selection and significantly decreased stimulated current. Results suggest that estimation method, accuracy, and number of channels in the proposed strategy using ideal formants may improve intelligibility when corresponding stimulated current of formant channels are not masked by noise-dominant channels.

Smartphone-Based Point-of-Care Urinalysis Assessment.

A dipstick urinalysis test is performed by immersing a reagent strip in the urine specimen and then comparing the resulting reagent pad colors with a reference key. The color assessment of the reagent strip can be performed manually or by using a urine analyzer. However, the manual procedure is prone to subjective inaccuracies in varying ambient illumination and urine analyzer equipment is expensive. This paper presents a smartphone-based machine-learning approach to accurately determine the reagent pad colors for automated assessment. We start with a unique calibration chart and use multivariate linear regression to map the captured color values to their true equivalents. This accounts for the camera-induced distortions and ambient illumination factors. Subsequently, the color comparison is performed using the least Euclidean distance to match the calibrated color of each reagent pad with the reference key. The results from an experimental study, using five different smartphone cameras and three common illumination settings, indicate a high degree of accuracy in color assessment for synthetic dipsticks. The proposed smartphone-based method is an easy-to-perform, time-efficient, and cost-effective solution for an automated urinalysis and could be used as an alternative to manual reading or benchtop urine analyzers. Clinical Relevance- The methods, technology, and data reported in this research can serve as an accurate, reliable, and cost-effective means for automated urinalysis in comparison to the existing methods. Furthermore, the ubiquity of smartphones opens new avenues for automated diagnostics in clinical, at-home, and point-of-care settings.

CCi-MOBILE: A Portable Real Time Speech Processing Platform for Cochlear Implant and Hearing Research.

Experimental hardware-research interfaces form a crucial role during developmental stages of any medical, signal-monitoring system as it allows researchers to test and optimize output results before perfecting the design for the actual FDA approved medical device and large-scale production. These testing platforms, intake raw signals through which performance of novel algorithms can be analyzed and modified to generate the desired data points for an optimized output, allowing the advancement of the medical device. With cochlear implants (CIs) and hearing aids (HAs) becoming a more common solution for varying degrees of hearing impairment, having modern signal processing strategies tested for such speech sensitive systems is a necessity. But the rigid design requirements of commercial CI and HA processors make it difficult to explore novel algorithms for research investigations and conducting longitudinal studies. This study presents the design, development, clinical evaluation, and applications of CCi-MOBILE, a computationally powerful signal processing testing platform built for researchers in the hearing-impaired field. The custom-made, portable research platform allows researchers to design and perform complex speech processing algorithm assessment offline and in real-time. It can be operated through user-friendly, open-source software and is compatible with implants manufactured by Cochlear Corporation. The FPGA design and hardware processing pipeline for CI stimulation is discussed followed by results from an acute study with implant users' speech intelligibility in quiet and noisy conditions. The results show a consistent level of performance compared with CI users' clinical processor, thus confirming the viability of the platform in chronic CI based studies.

Estimating hearing aid fitting presets with machine learning-based clustering strategies.

Although there exist nearly 35 × 106 hearing impaired people in the U.S., only an estimated 25% use hearing aids (HA), while others elect not to use prescribed HAs. Lack of HA acceptance can be attributed to several factors including (i) performance variability in diverse environments, (ii) time-to-convergence for best HA operating configuration, (iii) unrealistic expectations, and (iv) cost/insurance. This study examines a nationwide dataset of pure-tone audiograms and HA fitting configurations. An overview of data characteristics is presented, followed by use of machine learning clustering to suggest ways of obtaining effective starting configurations, thereby reducing time-to-convergence to improve HA retention.

A speech perturbation strategy based on "Lombard effect" for enhanced intelligibility for cochlear implant listeners.

The goal of this study is to determine potential intelligibility benefits from Lombard speech for cochlear implant (CI) listeners in speech-in-noise conditions. "Lombard effect" (LE) is the natural response of adjusting speech production via auditory feedback due to noise exposure within acoustic environments. To evaluate intelligibility performance of natural and artificially induced Lombard speech, a corpus was generated to create natural LE from large crowd noise (LCN) exposure at 70, 80, and 90 dB sound pressure level (SPL). Clean speech was mixed with 15 and 10 dB SNR LCN and presented to five CI users. First, speech intelligibility was analyzed as a function of increasing LE and decreasing SNR. Results indicate significant improvements (p < 0.05) with Lombard speech intelligibility in noise conditions for 80 and 90 dB SPL. Next, an offline perturbation strategy was formulated to modify/perturb neutral speech so as to mimic LE through amplification of highly intelligible segments, uniform time stretching, and spectral mismatch filtering. This process effectively introduces aspects of LE into the neutral speech, with the hypothesis that this would benefit intelligibility for CI users. Significant (p < 0.01) intelligibility improvements of 13% and 16% percentage points were observed for 15 and 10 dB SNR conditions respectively for CI users. The results indicate how LE and LE-inspired acoustic and frequency-based modifications can be leveraged within signal processing to improve intelligibility of speech for CI users.

CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers.

Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.

A Machine Learning Based Clustering Protocol for Determining Hearing Aid Initial Configurations from Pure-Tone Audiograms.

Of the nearly 35 million people in the USA who are hearing impaired, only an estimated 25% use hearing aids (HA). A good number of HAs are prescribed but not used partially because of the time to convergence for best operation between the audiologist and user. To improve HA retention, it is suggested that a machine learning (ML) protocol could be established which improves initial HA configurations given a user's pure-tone audiogram. This study examines a ML clustering method to predict the best initial HA fitting from a corpus of over 90,000 audiogram-fitting pairs collected from hearing centers throughout the USA. We first examine the final HA comfort targets to determine a limited number of preset configurations using several multi-dimensional clustering methods (Birch, Ward, and k-means). The goal is to reduce the amount of adjustments between the centroid, selected as a fitting configuration to represent the cluster, and the final HA configurations. This may be used to reduce the adjustment cycles for HAs or as preset starting configurations for personal sound amplification products (PSAPs). Using various classification methods, audiograms are mapped to a limited number of potential preset configurations. Finally, the average adjustment between the preset fitting targets and the final fitting targets is examined.

Formant priority channel selection for an "n-of-m" sound processing strategy for cochlear implants.

The Advanced Combination Encoder (ACE) signal processing strategy is used in the majority of cochlear implant (CI) sound processors manufactured by Cochlear Corporation. This "n-of-m" strategy selects "n" out of "m" available frequency channels with the highest spectral energy in each stimulation cycle. It is hypothesized that at low signal-to-noise ratio (SNR) conditions, noise-dominant frequency channels are susceptible for selection, neglecting channels containing target speech cues. In order to improve speech segregation in noise, explicit encoding of formant frequency locations within the standard channel selection framework of ACE is suggested. Two strategies using the direct formant estimation algorithms are developed within this study, FACE (formant-ACE) and VFACE (voiced-activated-formant-ACE). Speech intelligibility from eight CI users is compared across 11 acoustic conditions, including mixtures of noise and reverberation at multiple SNRs. Significant intelligibility gains were observed with VFACE over ACE in 5 dB babble noise; however, results with FACE/VFACE in all other conditions were comparable to standard ACE. An increased selection of channels associated with the second formant frequency is observed for FACE and VFACE. Both proposed methods may serve as potential supplementary channel selection techniques for the ACE sound processing strategy for cochlear implants.

Testing paradigms for assistive hearing devices in diverse acoustic environments.

Many individuals worldwide are at risk of hearing loss due to unsafe acoustical exposure and chronic listening experience using personal audio devices. Assistive hearing devices(AHD), such as hearing-aids(HAs) and cochlear-implants(CIs) are a common choice for the restoration and rehabilitation of the auditory function. Audio sound processors in CIs and HAs operate within limits, prescribed by audiologists, not only for acceptable sound perception but also for safety reasons. Signal processing(SP) engineers follow best design practices to ensure reliable performance and incorporate necessary safety checks within the design of SP strategies to ensure safety limits are never exceeded irrespective of acoustic environments. This paper proposes a comprehensive testing and evaluation paradigm to investigate the behavior of audio devices that addresses the safety concerns in diverse acoustic conditions. This is achieved by characterizing the performance of devices with large amounts of acoustic inputs and monitoring the output behavior. The CCi-MOBILE Research-Interface(RI) (used for CI/HA research) is used in this study as the testing paradigm. Factors such as pulse-width(PW), inter-phase gap(IPG) and a number of other parameters are estimated to evaluate the impact of AHDs on hearing comfort, subjective sound quality and characterize audio devices in terms of listening perception and biological safety.

The Lombard effect observed in speech produced by cochlear implant users in noisy environments: A naturalistic study.

The Lombard effect is an involuntary response speakers experience in the presence of noise during voice communication. This phenomenon is known to cause changes in speech production such as an increase in intensity, pitch structure, formant characteristics, etc., for enhanced audibility in noisy environments. Although well studied for normal hearing listeners, the Lombard effect has received little, if any, attention in the field of cochlear implants (CIs). The objective of this study is to analyze speech production of CI users who are postlingually deafened adults with respect to environmental context. A total of six adult CI users were recruited to produce spontaneous speech in various realistic environments. Acoustic-phonetic analysis was then carried out to characterize their speech production in these environments. The Lombard effect was observed in the speech production of all CI users who participated in this study in adverse listening environments. The results indicate that both suprasegmental (e.g., F0, glottal spectral tilt and vocal intensity) and segmental (e.g., F1 for /i/ and /u/) features were altered in such environments. The analysis from this study suggests that modification of speech production of CI users under the Lombard effect may contribute to some degree an intelligible communication in adverse noisy environments.

Evaluation and analysis of whispered speech for cochlear implant users: Gender identification and intelligibility.

This study investigates the degree to which whispered speech impacts speech perception and gender identification in cochlear implant (CI) users. Listening experiments with six CI subjects under neutral and whispered speech conditions using sentences from the UT-Vocal Effort II corpus (recordings from male and female speakers) were conducted. Results indicated a significant effect of whispering on gender identification and speech intelligibility scores. In addition, no significant effect of talker gender on the speech/gender identification scores was observed. Results also suggested that exposure to longer speech stimuli, and consequently more temporal cues, would not improve gender identification performance in CI subjects.

Evaluation of adaptive dynamic range optimization in adverse listening conditions for cochlear implants.

The aim of this study is to investigate the effect of Adaptive Dynamic Range Optimization (ADRO) on speech identification for cochlear implant (CI) users in adverse listening conditions. In this study, anechoic quiet, noisy, reverberant, noisy reverberant, and reverberant noisy conditions are evaluated. Two scenarios are considered when modeling the combined effects of reverberation and noise: (a) noise is added to the reverberant speech, and (b) noisy speech is reverberated. CI users were tested in different listening environments using IEEE sentences presented at 65 dB sound pressure level. No significant effect of ADRO processing on speech intelligibility was observed.

Design and evaluation of a personal digital assistant-based research platform for cochlear implants.

This paper discusses the design, development, features, and clinical evaluation of a personal digital assistant (PDA)-based platform for cochlear implant research. This highly versatile and portable research platform allows researchers to design and perform complex experiments with cochlear implants manufactured by Cochlear Corporation with great ease and flexibility. The research platform includes a portable processor for implementing and evaluating novel speech processing algorithms, a stimulator unit which can be used for electrical stimulation and neurophysiologic studies with animals, and a recording unit for collecting electroencephalogram/evoked potentials from human subjects. The design of the platform for real time and offline stimulation modes is discussed for electric-only and electric plus acoustic stimulation followed by results from an acute study with implant users for speech intelligibility in quiet and noisy conditions. The results are comparable with users' clinical processor and very promising for undertaking chronic studies.

On the design and evaluation of the PDA-based research platform for electric and acoustic stimulation.

The aim of the paper is to describe the bimodal (combining electrical stimulation via the implant with acoustic stimulation via hearing aids) design of the PDA-based research platform and present results from a short-term evaluation with five bimodal cochlear implant users. The evolution of the PDA platform has been reported earlier in terms of development and its potential in various experiments. This paper focuses on the evaluation of the platform with bimodal users in terms of speech intelligibility in quiet, 10dB and 5dB SNR conditions and compares the results with the users' own clinical processor. The results of this clinical trial will encourage researchers in this area to use the platform in their future studies as it provides unparalleled flexibility along with a large suite of applications to conduct a wide variety of experiments for electric-only and combined electric and acoustic stimulation (EAS) for long-term chronic studies with great ease.

A PDA platform for offline processing and streaming of stimuli for cochlear implant research.

A PDA-based research platform has been developed for implementing novel speech processing strategies and conducting psychophysical experiments with cochlear implant (CI) research that do not necessarily require real-time processing. The developed interface streams stimuli pulses to a CI unit in an offline mode from a Personal Computer via PDA platform using Windows Sockets (WINSOCK). Front-end of the application is run in MATLAB where stimuli pulses are created. Winsock establishes a TCP/IP connection with the PDA and starts the transmission of stimuli data. Server application installed on the PDA reads the stimulation data and forwards it to the SDIO board in packets where it is forwarded to the cochlear implant unit and pulses are then played in realtime. Versatility and flexibility are the key characteristics of the platform for easy implementation and testing of a wide range of applications and experiments without advanced programming skills.

Effect of atrioventricular conduction on heart rate variability.

This paper discusses the effect of atrioventricular conduction time (AVCT) on the short-term Heart Rate Variability (HRV) by computing HRV parameters using intervals between the onsets of successive P waves (PP time series) for three groups: normal, arrhythmia and sudden cardiac death (SCD) patients. A very precise wavelet transform based ECG delineator was developed to detect PP, PR and RR time series. Mean PR variation in arrhythmia and SCD group was found to be significantly high as compared to the normal group. It was observed that when PR variations in arrhythmia and SCD cases crossed a certain threshold, RR variability no longer provided a very accurate estimate of HRV. In such cases, PP variability was able to provide a better assessment of HRV.

Laboratory prototype of cochlear implant: design and techniques.

This paper presents design overview of a low cost prototype of Cochlear Implant developed from commercial off-the-shelf components. Design scope includes speech processing module implemented on a commercial digital signal processor, transcutaneous data and power transceiver developed from a single pair of inductive coils and finally a stimulator circuitry for cochlear stimulation. Different speech processing strategies such as CIS, SMSP and F0/F1 have been implemented and tested using a novel, indigenously developed speech processing research module which evaluates the performance of speech processing strategies in software, hardware and practical scenarios. Design overview, simulations and practical results of an optimized inductive link using Class E Power Amplifier are presented. Link was designed at a carrier frequency of 2.5MHz for 100mW output power. Receiver logic design and stimulator circuitry was implemented using a PIC microcontroller and off-the-shelf electronic components. Results indicate 40% link efficiency with 128kbps data transfer rate. This low cost prototype can be used for undertaking cochlear implant research in laboratories.

Efficient algorithm development of CIS speech processing strategy for cochlear implants.

Continuous Interleaved Sampling (CIS) is one of the most useful and famous speech processing strategies used in Cochlear Implant speech processors. However, algorithm realization in hardware is a laborious task due to high computation cost of the algorithm. Real-time issues and low-power design demands an optimized realization of algorithm. This paper proposes two techniques to cut the computation cost of CIS by using polyphase filters and by implementing the complete algorithm in frequency domain. About 70% reduction in computation cost can be achieved by using multi-rate, multistage filters; whereas computation cost decreases by a factor of five when the whole algorithm is implemented in frequency domain. Evaluation of the algorithm is done by a laboratory designed algorithm development and evaluation platform. Algorithm flow diagrams and their computation details have been given for comparison. Utilizing the given techniques can remarkably reduce the processor load without any compromise on quality.