Voice disorder recognition using machine learning: a scoping review protocol.

INTRODUCTION: Over the past decade, several machine learning (ML) algorithms have been investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML algorithms can detect voice disorders with high accuracy. This suggests that ML has the potential to assist clinicians in the analysis and treatment outcome evaluation of voice disorders. However, despite numerous research studies, none of the algorithms have been sufficiently reliable to be used in clinical settings. Through this review, we aim to identify critical issues that have inhibited the use of ML algorithms in clinical settings by identifying standard audio tasks, acoustic features, processing algorithms and environmental factors that affect the efficacy of those algorithms. METHODS: We will search the following databases: Web of Science, Scopus, Compendex, CINAHL, Medline, IEEE Explore and Embase. Our search strategy has been developed with the assistance of the university library staff to accommodate the different syntactical requirements. The literature search will include the period between 2013 and 2023, and will be confined to articles published in English. We will exclude editorials, ongoing studies and working papers. The selection, extraction and analysis of the search data will be conducted using the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews' system. The same system will also be used for the synthesis of the results. ETHICS AND DISSEMINATION: This scoping review does not require ethics approval as the review solely consists of peer-reviewed publications. The findings will be presented in peer-reviewed publications related to voice pathology.

An investigation into the effectiveness of using acoustic touch to assist people who are blind.

Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer's surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as "acoustic touch" to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device's field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user's cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.

Nano-Enhanced Drug Delivery and Therapeutic Ultrasound for Cancer Treatment and Beyond.

While ultrasound is most widely known for its use in diagnostic imaging, the energy carried by ultrasound waves can be utilized to influence cell function and drug delivery. Consequently, our ability to use ultrasound energy at a given intensity unlocks the opportunity to use the ultrasound for therapeutic applications. Indeed, in the last decade ultrasound-based therapies have emerged with promising treatment modalities for several medical conditions. More recently, ultrasound in combination with nanomedicines, i.e., nanoparticles, has been shown to have substantial potential to enhance the efficacy of many treatments including cancer, Alzheimer disease or osteoarthritis. The concept of ultrasound combined with drug delivery is still in its infancy and more research is needed to unfold the mechanisms and interactions of ultrasound with different nanoparticles types and with various cell types. Here we present the state-of-art in ultrasound and ultrasound-assisted drug delivery with a particular focus on cancer treatments. Notably, this review discusses the application of high intensity focus ultrasound for non-invasive tumor ablation and immunomodulatory effects of ultrasound, as well as the efficacy of nanoparticle-enhanced ultrasound therapies for different medical conditions. Furthermore, this review presents safety considerations related to ultrasound technology and gives recommendations in the context of system design and operation.

Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented.

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.

Convolutional neural network for single-sensor acoustic localization of a transiting broadband source in very shallow water.

When a broadband source of radiated noise transits past a fixed hydrophone, a Lloyd's mirror constructive/destructive interference pattern can be observed in the output spectrogram. By taking the spectrum of a (log) spectrum, the power cepstrum detects the periodic structure of the Lloyd's mirror fringe pattern by generating a sequence of pulses located at the fundamental quefrency and its multiples. The fundamental quefrency, which is the reciprocal of the frequency difference between adjacent destructive interference fringes, equates to the multipath delay time. An experiment is conducted where a motorboat transits past a hydrophone located about 1 m above the seafloor in very shallow water (20 m deep). The hydrophone has a frequency bandwidth of 90 kHz, and its output is sampled at 250 kHz. A cepstrogram database is compiled from multiple vessel transits, and its cepstrum-based feature vectors (along with ground-truth range data) form the input to train a convolutional neural network (CNN) so that it can predict the source range relative to the hydrophone for other ("unseen") vessel transits. The CNN provides an accurate prediction of the instantaneous source range even when the range estimate from conventional multipath passive ranging is biased, which occurs at low grazing angles (<12°).

Length-free near infrared measurement of newborn malnutrition.

Under-nutrition in neonates can cause immediate mortality, impaired cognitive development and early onset adult disease. Body fat percentage measured using air-displacement-plethysmography has been found to better indicate under-nutrition than conventional birth weight percentiles. However, air-displacement-plethysmography equipment is expensive and non-portable, so is not suited for use in developing communities where the burden is often the greatest. We proposed a new body fat measurement technique using a length-free model with near-infrared spectroscopy measurements on a single site of the body - the thigh. To remove the need for length measurement, we developed a model with five discrete wavelengths and a sex parameter. The model was developed using air-displacement-plethysmography measurements in 52 neonates within 48 hours of birth. We identified instrumentation required in a low-cost LED-based screening device and incorporated a receptor device that can increase the amount of light collected. This near-infrared method may be suitable as a low cost screening tool for detecting body fat levels and monitoring nutritional interventions for malnutrition in neonates and young children in resource-constrained communities.

Electrical impedance tomography for assessing ventilation/perfusion mismatch for pulmonary embolism detection without interruptions in respiration.

Recent studies have shown high correlation between pulmonary perfusion mapping with impedance contrast enhanced Electrical Impedance Tomography (EIT) and standard perfusion imaging methods such as Computed Tomography (CT) and Single Photon Emission Computerized Tomography (SPECT). EIT has many advantages over standard imaging methods as it is highly portable and non-invasive. Contrast enhanced EIT uses hypertonic saline bolus instead of nephrotoxic contrast medium that are utilized by CT and nuclear Ventilation/Perfusion (V/Q) scans. However, current implementation of contrast enhanced EIT requires induction of an apnea period for perfusion measurement, rendering it disadvantageous compared with current gold standard imaging modalities. In the present paper, we propose the use of a wavelet denoising algorithm to separate perfusion signal from ventilation signal such that no interruption in patient's ventilation would be required. Furthermore, right lung to left lung perfusion ratio and ventilation ratio are proposed to assess the mismatch between ventilation and perfusion for detection of Pulmonary Embolism (PE). The proposed methodology was validated on an ovine model (n=3, 83.7±7.7 kg) with artificially induced PE in the right lung. The results showed a difference in right lung to left lung perfusion ratio between baseline and diseased states in all cases with all paired t-tests between baseline and PE yielding p <; 0.01, while the right lung to left lung ventilation ratio remained unchanged in two out of three experiments. Statistics were pooled from multiple repetitions of measurements per experiment.

Towards true unipolar bio-potential recording: a preliminary result for ECG.

We present a bio-potential front-end capable of recording unipolar ECG leads without making use of the Wilson central terminal (WCT). The information contained in the new unipolar recordings may yield unique diagnostic information as it avoids the need to essentially subtract data or make use of the averaging effect imposed by the WCT. The system also allows a direct, real-time software calculation of signals corresponding to standard ECG leads for standard diagnosis. These calculated standard ECG leads have a correlation in excess of 92% with a gold standard ECG recorded in parallel. The circuit is wideband, compatible with both the standard and the dry electrodes, and of low power (requiring less than 20 mW powered at 12 V). It is therefore well suited for long-term applications.

Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study.

Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous µ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone.

Pregnancy detection and monitoring in cattle via combined foetus electrocardiogram and phonocardiogram signal processing.

BACKGROUND: Pregnancy testing in cattle is commonly invasive requiring manual rectal palpation of the reproductive tract that presents risks to the operator and pregnancy. Alternative non-invasive tests have been developed but have not gained popularity due to poor specificity, sensitivity and the inconvenience of sample handling. Our aim is to present the pilot study and proof of concept of a new non invasive technique to sense the presence and age (limited to the closest trimester of pregnancy) of the foetus by recording the electrical and audio signals produced by the foetus heartbeat using an array of specialized sensors embedded in a stand alone handheld prototype device. The device was applied to the right flank (approximately at the intercept of a horizontal line drawn through the right mid femur region of the cow and a vertical line drawn anywhere between lumbar vertebrae 3 to 5) of more than 2000 cattle from 13 different farms, including pregnant and not pregnant, a diversity of breeds, and both dairy and beef herds. Pregnancy status response is given "on the spot" from an optimized machine learning algorithm running on the device within seconds after data collection. RESULTS: Using combined electrical and audio foetal signals we detected pregnancy with a sensitivity of 87.6% and a specificity of 74.6% for all recorded data. Those values increase to 91% and 81% respectively by removing files with excessive noise (19%).Foetus ageing was achieved by comparing the detected foetus heart-rate with published tables. However, given the challenging farm environment of a restless cow, correct foetus ageing was achieved for only 21% of the correctly diagnosed pregnant cows. CONCLUSIONS: In conclusion we have found that combining ECG and PCG measurements on the right flank of cattle provides a reliable and rapid method of pregnancy testing. The device has potential to be applied by unskilled operators. This will generate more efficient and productive management of farms. There is potential for the device to be applied to large endangered quadrupeds in captive breeding programs where early, safe and reliable pregnancy diagnosis can be imperative but currently difficult to achieve.

Electrode contact impedance sensitivity to variations in geometry.

Electrode contact impedance is a crucial factor in physiological measurements and can be an accuracy-limiting factor when performing electroencephalography and electrical impedance tomography. In this work, standard flat electrodes and micromachined multipoint spiked electrodes are characterized with a finite-element method electromagnetic solver and the dependence of the contact impedance on geometrical factors is explored. It is found that flat electrodes are sensitive to changes in the outer skin layer properties related to hydration and thickness, while spike electrodes are not. The impedance as a function of the effective contact area, number of spikes and penetration depth has also been studied and characterized.

Neuromorphic audio-visual sensor fusion on a sound-localizing robot.

This paper presents the first robotic system featuring audio-visual (AV) sensor fusion with neuromorphic sensors. We combine a pair of silicon cochleae and a silicon retina on a robotic platform to allow the robot to learn sound localization through self motion and visual feedback, using an adaptive ITD-based sound localization algorithm. After training, the robot can localize sound sources (white or pink noise) in a reverberant environment with an RMS error of 4-5° in azimuth. We also investigate the AV source binding problem and an experiment is conducted to test the effectiveness of matching an audio event with a corresponding visual event based on their onset time. Despite the simplicity of this method and a large number of false visual events in the background, a correct match can be made 75% of the time during the experiment.

Measurement of retinal arteriolar diameters from auto scale phase congruency with fuzzy weighting and L1 regularization.

Manual measurements of small changes in retinal vascular diameter are slow and may be subject to considerable observer-related biases. Among the conventional automatic methods the sliding linear regression filter (SLRF) demonstrates the least scattered and most repeatable coefficients. For optimal performance it relies on the choice of the correct filter scale for different vessel sizes. A small scale extracts fine details at the expense noise sensitivity, while large scales have poor edge localization. Here we use auto scale phase congruency to select the filter scales with fuzzy weighting to reduce noise, and L1 regularization for edge smoothing. Our method uses a one dimensional analysis normal to the vessel and so is faster than the 2D phase congruency. In 65 vessels randomly selected from 20 images the proposed method showed better repeatability and over three times less scattering than conventional SLRF.

Comparison of the measured and theoretical performance of a broadband circular microphone array.

The design and construction of a circular microphone array (CMA) that has a wide frequency range suitable for human hearing is presented. The design of the CMA was achieved using a technique based on simulated directivity index (DI) curves. The simulated DI curves encapsulate the critical microphone array performance limitations: spatial aliasing, measurement noise, and microphone placement errors. This paper demonstrates how the non-regularized DI curves for a given beamforming order clearly define the bandwidth of operation, in other words, the frequency band for which the beamformer has relatively constant and maximum directivity. Detailed and comprehensive experimental data that characterizes the CMA beamformer are also presented.

Wearable dry sensors with bluetooth connection for use in remote patient monitoring systems.

Cost reduction has become the primary theme of healthcare reforms globally. More providers are moving towards remote patient monitoring, which reduces the length of hospital stays and frees up their physicians and nurses for acute cases and helps them to tackle staff shortages. Physiological sensors are commonly used in many human specialties e.g. electrocardiogram (ECG) electrodes, for monitoring heart signals, and electroencephalogram (EEG) electrodes, for sensing the electrical activity of the brain, are the most well-known applications. Consequently there is a substantial unmet need for physiological sensors that can be simply and easily applied by the patient or primary carer, are comfortable to wear, can accurately sense parameters over long periods of time and can be connected to data recording systems using Bluetooth technology. We have developed a small, battery powered, user customizable portable monitor. This prototype is capable of recording three-axial body acceleration, skin temperature, and has up to four bio analogical front ends. Moreover, it is also able of continuous wireless transmission to any Bluetooth device including a PDA or a cellular phone. The bio-front end can use long-lasting dry electrodes or novel textile electrodes that can be embedded in clothes. The device can be powered by a standard mobile phone which has a Ni-MH 3.6 V battery, to sustain more than seven days continuous functioning when using the Bluetooth Sniff mode to reduce TX power. In this paper, we present some of the evaluation experiments of our wearable personal monitor device with a focus on ECG applications.

Adaptive sound localization with a silicon cochlea pair.

A neuromorphic sound localization system is presented. It employs two microphones and a pair of silicon cochleae with address event interface for front-end processing. The system is based the extraction of interaural time difference from a far-field source. At each frequency channel, a soft-winner-takes-all network is used to preserve timing information before it is processed by a simple neural network to estimate auditory activity at all bearing positions. The estimates are then combined across channels to produce the final estimate. The proposed algorithm is adaptive and supports online learning, enabling the system to compensate for circuit mismatch and environmental changes. Its localization capability was tested with white noise and pure tone stimuli, with an average error of around 3° in the -45° to 45° range.

A comparison between compressed sensing algorithms in electrical impedance tomography.

Electrical Impedance Tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. Conventional EIT reconstruction methods solve a linear model by minimizing the least squares error, i.e., the Euclidian or L2-norm, with regularization. Compressed sensing provides unique advantages in Magnetic Resonance Imaging (MRI) [1] when the images are transformed to a sparse basis. EIT images are generally sparser than MRI images due to their lower spatial resolution. This leads us to investigate ability of compressed sensing algorithms currently applied to MRI in EIT without transformation to a new basis. In particular, we examine four new iterative algorithms for L1 and L0 minimization with applications to compressed sensing and compare these with current EIT inverse L1-norm regularization methods. The four compressed sensing methods are as follows: (1) an interior point method for solving L1-regularized least squares problems (L1-LS); (2) total variation using a Lagrangian multiplier method (TVAL3); (3) a two-step iterative shrinkage / thresholding method (TWIST) for solving the L0-regularized least squares problem; (4) The Least Absolute Shrinkage and Selection Operator (LASSO) with tracing the Pareto curve, which estimates the least squares parameters subject to a L1-norm constraint. In our investigation, using 1600 elements, we found all four CS algorithms provided an improvement over the best conventional EIT reconstruction method, Total Variation, in three important areas: robustness to noise, increased computational speed of at least 40x and a visually apparent improvement in spatial resolution. Out of the four CS algorithms we found TWIST was the fastest with at least a 100x speed increase.

Dry electrode bio-potential recordings.

As wireless bio-medical long term monitoring moves towards personal monitoring it demands very high input impedance systems capable to extend the reading of bio-signal during the daily activities offering a kind of "stress free", convenient connection, with no need for skin preparation. In particular we highlight the development and broad applications of our own circuits for wearable bio-potential sensor systems enabled by the use of an FET based amplifier circuit with sufficiently high impedance to allow the use of passive dry electrodes which overcome the significant barrier of gel based contacts. In this paper we present the ability of dry electrodes in long term monitoring of ECG, EEG and fetal ECG.

A new EEG recording system for passive dry electrodes.

OBJECTIVE: We present a new, low power EEG recording system with an ultra-high input impedance that enables the use of long-lasting, passive dry electrodes. It incorporates Bluetooth wireless connectivity and is designed to be suitable for long-term monitoring during daily activities. METHODS: The new EEG system is compared to a standard and clinically available reference EEG system using wet electrodes in three separate sets of experiments. In the first two experiments, each dry electrode was surrounded by four standard wet electrodes and the alpha and mu-rhythms were recorded. In the third experiment, serial monopolar (referred to the left ear) recordings of flash visual evoked potential were performed using the new EEG system and a reference system. RESULTS: These experiments showed that the signal recorded using the new EEG system is almost identical to that recorded with standard clinical EEG equipment; our measurements showed that the correlation coefficient between the dry electrode recordings and the average of the four standard electrodes surrounding each dry electrode is greater than 0.85. CONCLUSION: We conclude that the new EEG system performs similarly to reference EEG systems, while providing the advantages of portability, ease of application and minimal scalp preparation. SIGNIFICANCE: The proposed system using passive dry electrodes suitable for single use while performing as good as standard EEG equipment provides ease of application and minimal scalp preparation.

An ultra-high input impedance ECG amplifier for long-term monitoring of athletes.

We present a new, low-power electrocardiogram (ECG) recording system with an ultra-high input impedance that enables the use of long-lasting, dry electrodes. The system incorporates a low-power Bluetooth module for wireless connectivity and is designed to be suitable for long-term monitoring during daily activities. The new system using dry electrodes was compared with a clinically approved ECG reference system using gelled Ag/AgCl electrodes and performance was found to be equivalent. In addition, the system was used to monitor an athlete during several physical tasks, and a good quality ECG was obtained in all cases, including when the athlete was totally submerged in fresh water.