Assessing Speech Audibility via Syllabic-Rate Neural Responses in Adults and Children With and Without Hearing Loss.

An objective method for assessing speech audibility is essential to evaluate hearing aid benefit in children who are unable to participate in hearing tests. With consonant-vowel syllables, brainstem-dominant responses elicited at the voice fundamental frequency have proven successful for assessing audibility. This study aimed to harness the neural activity elicited by the slow envelope of the same repetitive consonant-vowel syllables to assess audibility. In adults and children with normal hearing and children with hearing loss wearing hearing aids, neural activity elicited by the stimulus /su∫i/ or /sa∫i/ presented at 55-75 dB SPL was analyzed using the temporal response function approach. No-stimulus runs or very low stimulus level (15 dB SPL) were used to simulate inaudible conditions in adults and children with normal hearing. Both groups of children demonstrated higher response amplitudes relative to adults. Detectability (sensitivity; true positive rate) ranged between 80.1 and 100%, and did not vary by group or stimulus level but varied by stimulus, with /sa∫i/ achieving 100% detectability at 65 dB SPL. The average minimum time needed to detect a response ranged between 3.7 and 6.4 min across stimuli and listener groups, with the shortest times recorded for stimulus /sa∫i/ and in children with hearing loss. Specificity was >94.9%. Responses to the slow envelope of non-meaningful consonant-vowel syllables can be used to ascertain audible vs. inaudible speech with sufficient accuracy within clinically feasible test times. Such responses can increase the clinical usefulness of existing objective approaches to evaluate hearing aid benefit.

Predicting health-related quality of life change using natural language processing in thyroid cancer.

Background: Patient-reported outcomes (PRO) allow clinicians to measure health-related quality of life (HRQOL) and understand patients' treatment priorities, but obtaining PRO requires surveys which are not part of routine care. We aimed to develop a preliminary natural language processing (NLP) pipeline to extract HRQOL trajectory based on deep learning models using patient language. Materials and methods: Our data consisted of transcribed interviews of 100 patients undergoing surgical intervention for low-risk thyroid cancer, paired with HRQOL assessments completed during the same visits. Our outcome measure was HRQOL trajectory measured by the SF-12 physical and mental component scores (PCS and MCS), and average THYCA-QoL score.We constructed an NLP pipeline based on BERT, a modern deep language model that captures context semantics, to predict HRQOL trajectory as measured by the above endpoints. We compared this to baseline models using logistic regression and support vector machines trained on bag-of-words representations of transcripts obtained using Linguistic Inquiry and Word Count (LIWC). Finally, given the modest dataset size, we implemented two data augmentation methods to improve performance: first by generating synthetic samples via GPT-2, and second by changing the representation of available data via sequence-by-sequence pairing, which is a novel approach. Results: A BERT-based deep learning model, with GPT-2 synthetic sample augmentation, demonstrated an area-under-curve of 76.3% in the classification of HRQOL accuracy as measured by PCS, compared to the baseline logistic regression and bag-of-words model, which had an AUC of 59.9%. The sequence-by-sequence pairing method for augmentation had an AUC of 71.2% when used with the BERT model. Conclusions: NLP methods show promise in extracting PRO from unstructured narrative data, and in the future may aid in assessing and forecasting patients' HRQOL in response to medical treatments. Our experiments with optimization methods suggest larger amounts of novel data would further improve performance of the classification model.

Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence.

Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject's motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target's motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework.

Cooperative Networked PIR Detection System for Indoor Human Localization.

Pyroelectric Infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems can be categorized as wearable and non-wearable systems, where the latter are also known as device-free localization systems. Since the binary PIR sensor detects only the presence of a human motion in its field of view (FOV) without any other information about the actual location, utilizing the information of overlapping FOV of multiple sensors can be useful for localization. In this study, a PIR detector and sensing signal processing algorithms were designed based on the characteristics of the PIR sensor. We applied the designed PIR detector as a sensor node to create a non-wearable cooperative indoor human localization system. To improve the system performance, signal processing algorithms and refinement schemes (i.e., the Kalman filter, a Transferable Belief Model, and a TBM-based hybrid approach (TBM + Kalman filter)) were applied and compared. Experimental results indicated system stability and improved positioning accuracy, thus providing an indoor cooperative localization framework for PIR sensor networks.

Design of Carryable Intravenous Drip Frame with Automatic Balancing.

Due to the inconvenience of the conventional intravenous drip frame, the piggyback intravenous drip frame is developed to improve the mobility of the patient. However, the current design of the drip frame leads to a lack of balance control and increment of blood returning. To this end, the proposed system aims to solve this problem, and a fuzzy proportionalintegral-derivative control technique is developed to demonstrate the system feasibility. Accordingly, a reliable balanced system can be applied to facilitate patients' movements and ensure patient safety with compensating the inclination angle of the drip frame such that the reduction of blood returning and the balance control of the piggyback intravenous drip frame can be achieved.

Rethinking Measures of Functional Connectivity via Feature Extraction.

Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson's correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.

Engineering induction of singular neural rosette emergence within hPSC-derived tissues.

Human pluripotent stem cell (hPSC)-derived neural organoids display unprecedented emergent properties. Yet in contrast to the singular neuroepithelial tube from which the entire central nervous system (CNS) develops in vivo, current organoid protocols yield tissues with multiple neuroepithelial units, a.k.a. neural rosettes, each acting as independent morphogenesis centers and thereby confounding coordinated, reproducible tissue development. Here, we discover that controlling initial tissue morphology can effectively (>80%) induce single neural rosette emergence within hPSC-derived forebrain and spinal tissues. Notably, the optimal tissue morphology for observing singular rosette emergence was distinct for forebrain versus spinal tissues due to previously unknown differences in ROCK-mediated cell contractility. Following release of geometric confinement, the tissues displayed radial outgrowth with maintenance of a singular neuroepithelium and peripheral neuronal differentiation. Thus, we have identified neural tissue morphology as a critical biophysical parameter for controlling in vitro neural tissue morphogenesis furthering advancement towards biomanufacture of CNS tissues with biomimetic anatomy and physiology.

Efficient 3-D Reconstruction in Ultrasound Elastography via a Sparse Iteration Based on Markov Random Fields.

Percutaneous needle-based liver ablation procedures are becoming increasingly common for the treatment of small isolated tumors in hepatocellular carcinoma patients who are not candidates for surgery. Rapid 3-D visualization of liver ablations has potential clinical value, because it can enable interventional radiologists to plan and execute needle-based ablation procedures with real time feedback. Ensuring the right volume of tissue is ablated is desirable to avoid recurrence of tumors from residual untreated cancerous cells. Shear wave velocity (SWV) measurements can be used as a surrogate for tissue stiffness to distinguish stiffer ablated regions from softer untreated tissue. This paper extends the previously reported sheaf reconstruction method to generate complete 3-D visualizations of SWVs without resorting to an approximate intermediate step of reconstructing transverse C planes. The noisy data are modeled using a Markov random field, and a computationally tractable reconstruction algorithm that can handle grids with millions of points is developed. Results from simulated ellipsoidal inclusion data show that this algorithm outperforms standard nearest neighbor interpolation by an order of magnitude in mean squared reconstruction error. Results from the phantom experiments show that it also provides a higher contrast-to-noise ratio by almost 2 dB and better signal-to-noise ratio in the stiff inclusion by over 2 dB compared with nearest neighbor interpolation and has lower computational complexity than linear and spline interpolation.

Conditional Granger causality and partitioned Granger causality: differences and similarities.

Neural information modeling and analysis often requires a measurement of the mutual influence among many signals. A common technique is the conditional Granger causality (cGC) which measures the influence of one time series on another time series in the presence of a third. Geweke has translated this condition into the frequency domain and has explored the mathematical relationships between the time and frequency domain expressions. Chen has observed that in practice, the expressions may return (meaningless) negative numbers, and has proposed an alternative which is based on a partitioned matrix scheme, which we call partitioned Granger causality (pGC). There has been some confusion in the literature about the relationship between cGC and pGC; some authors treat them as essentially identical measures, while others have noted that some properties (such as the relationship between the time and frequency domain expressions) do not hold for the pGC. This paper presents a series of matrix equalities that simplify the calculation of the pGC. In this simplified expression, the essential differences and similarities between the cGC and the pGC become clear; in essence, the pGC is dependent on only a subset of the parameters in the model estimation, and the noise residuals (which are uncorrelated in the cGC) need not be uncorrelated in the pGC. The mathematical results are illustrated with a simulation, and the measures are applied to an EEG dataset.

Slope Estimation in Noisy Piecewise Linear Functions.

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure.

Spatial and frequency-based super-resolution of ultrasound images.

Modern ultrasound systems can output video images containing more spatial and temporal information than still images. Super-resolution techniques can exploit additional information but face two challenges: image registration and complex motion. In addition, information from multiple available frequencies is unexploited. Herein, we utilised these information sources to create better ultrasound images and videos, extending existing technologies for image capture. Spatial and frequency-based super-resolution processing using multiple motion estimation and frequency combination was applied to ultrasound videos of deforming models. Processed images are larger, have greater clarity and detail, and less variability in intensity between frames. Significantly, strain measurements are more accurate and precise than those from raw videos, and have a higher contrast ratio between 'tumour' and 'surrounding tissue' in a phantom model. We attribute improvements to reduced noise and increased resolution in processed images. Our methods can significantly improve quantitative and qualitative assessments of ultrasound images when compared assessments of standard images.

Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours.

Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase (512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.

Stochastic piecewise linear function fitting with application to ultrasound shear wave imaging.

Piecewise linear function fitting is ubiquitous in many signal processing applications. Inspired by an application to shear wave velocity imaging in ultrasound elastography, this paper presents a discrete state-space Markov model for noisy piecewise linear data and also proposes a tractable algorithm for maximum a posteriori estimation of the slope of each segment in the piecewise linear function. The number and locations of breaks is handled indirectly by the stochastics of the Markov model. In the ultrasound shear wave imaging application, these slope values have concrete physical interpretation as being the reciprocal of the shear wave velocities in the imaged medium. Data acquired on an ellipsoidal inclusion phantom shows that this algorithm can provide good contrast of around 6 dB and contrast to noise ratio of 25 dB between the stiff inclusion and surrounding soft background. The phantom validation study also shows that this algorithm can be used to preserve sharp boundary details, which would otherwise be blurred out if a sliding window least squares filter is applied.