Prediction of oxygen uptake kinetics during heavy-intensity cycling exercise by machine learning analysis.

Nonintrusive estimation of oxygen uptake (V̇o2) is possible with wearable sensor technology and artificial intelligence. V̇o2 kinetics have been accurately predicted during moderate exercise using easy-to-obtain sensor inputs. However, V̇o2 prediction algorithms for higher-intensity exercise with inherent nonlinearities are still being refined. The purpose of this investigation was to test if a machine learning model can accurately predict dynamic V̇o2 across exercise intensities, including slower V̇O2 kinetics normally observed during heavy- compared with moderate-intensity exercise. Fifteen young healthy adults (seven females; peak V̇o2: 42 ± 5 mL·min-1·kg-1) performed three different pseudorandom binary sequence (PRBS) exercise tests ranging in intensity from low-to-moderate, low-to-heavy, and ventilatory threshold-to-heavy work rates. A temporal convolutional network was trained to predict instantaneous V̇o2, with model inputs including heart rate, percent heart rate reserve, estimated minute ventilation, breathing frequency, and work rate. Frequency domain analyses between V̇o2 and work rate were used to evaluate measured and predicted V̇o2 kinetics. Predicted V̇o2 had low bias (-0.017 L·min-1, 95% limits of agreement: [-0.289, 0.254]), and was very strongly correlated (rrm = 0.974, P < 0.001) with the measured V̇o2. The extracted indicator of kinetics, mean normalized gain (MNG), was not different between predicted and measured V̇o2 responses (main effect: P = 0.374, ηp2 = 0.01), and decreased with increasing exercise intensity (main effect: P < 0.001, ηp2 = 0.64). Predicted and measured V̇o2 kinetics indicators were moderately correlated across repeated measurements (MNG: rrm = 0.680, P < 0.001). Therefore, the temporal convolutional network accurately predicted slower V̇o2 kinetics with increasing exercise intensity, enabling nonintrusive monitoring of cardiorespiratory dynamics across moderate- and heavy-exercise intensities.NEW & NOTEWORTHY Machine learning analysis of wearable sensor data with a sequential model, which utilized a receptive field of approximately 3 min to make instantaneous oxygen uptake estimations, accurately predicted oxygen uptake kinetics from moderate through to higher-intensity exercise. This innovation will enable nonintrusive cardiorespiratory monitoring over a wide range of exercise intensities encountered in vigorous training and competitive sports.

Characterizing Canadian long-term care home consumed foods and their inflammatory potential: a secondary analysis.

BACKGROUND: Nutrient dense food that supports health is a goal of food service in long-term care (LTC). The objective of this work was to characterize the "healthfulness" of foods in Canadian LTC and inflammatory potential of the LTC diet and how this varied by key covariates. Here, we define foods to have higher "healthfulness" if the are in accordance with the evidence-based 2019 Canada's Food Guide, or with comparatively lower inflammatory potential. METHODS: We conducted a secondary analysis of the Making the Most of Mealtimes dataset (32 LTC homes; four provinces). A novel computational algorithm categorized food items from 3-day weighed food records into 68 expert-informed categories and Canada's Food Guide (CFG) food groups. The dietary inflammatory potential of these food sources was assessed using the Dietary Inflammatory Index (DII). Comparisons were made by sex, diet texture, and nutritional status. RESULTS: Consumption patterns using expert-informed categories indicated no single protein or vegetable source was among the top 5 most commonly consumed foods. In terms of CFG's groups, protein food sources (i.e., foods with a high protein content) represented the highest proportion of daily calorie intake (33.4%; animal-based: 31.6%, plant-based: 1.8%), followed by other foods (31.3%) including juice (9.8%), grains (25.0%; refined: 15.0%, whole: 10.0%), and vegetables/fruits (10.3%; plain: 4.9%, with additions: 5.4%). The overall DII score (mean, IQR) was positive (0.93, 0.23 to 1.75) indicating foods consumed tend towards a pro-inflammatory response. DII was significantly associated with sex (female higher; p<0.0001), and diet (minced higher; p=0.036). CONCLUSIONS: "Healthfulness" of Canadian LTC menus may be enhanced by lowering inflammatory potential to support chronic disease management through further shifts from refined to whole grains, incorporating more plant-based proteins, and moving towards serving plain vegetables and fruits. However, there are multiple layers of complexities to consider when optimising foods aligned with the CFG, and shifting to foods with anti-inflammatory potential for enhanced health benefits, while balancing nutrition and ensuring sufficient food and fluid intake to prevent or treat malnutrition.

Enhancing Food Intake Tracking in Long-term Care With Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology: Validation and Feasibility Assessment.

BACKGROUND: Half of long-term care (LTC) residents are malnourished, leading to increased hospitalization, mortality, and morbidity, with low quality of life. Current tracking methods are subjective and time-consuming. OBJECTIVE: This paper presented the automated food imaging and nutrient intake tracking technology designed for LTC. METHODS: A needs assessment was conducted with 21 participating staff across 12 LTC and retirement homes. We created 2 simulated LTC intake data sets comprising modified (664/1039, 63.91% plates) and regular (375/1039, 36.09% plates) texture foods. Overhead red-green-blue-depth images of plated foods were acquired, and foods were segmented using a pretrained food segmentation network. We trained a novel convolutional autoencoder food feature extractor network using an augmented UNIMIB2016 food data set. A meal-specific food classifier was appended to the feature extractor and tested on our simulated LTC food intake data sets. Food intake (percentage) was estimated as the differential volume between classified full portion and leftover plates. RESULTS: The needs assessment yielded 13 nutrients of interest, requirement for objectivity and repeatability, and account for real-world environmental constraints. For 12 meal scenarios with up to 15 classes each, the top-1 classification accuracy was 88.9%, with mean intake error of -0.4 (SD 36.7) mL. Nutrient intake estimation by volume was strongly linearly correlated with nutrient estimates from mass (r2=0.92-0.99), with good agreement between methods (σ=-2.7 to -0.01; 0 within each of the limits of agreement). CONCLUSIONS: The automated food imaging and nutrient intake tracking approach is a deep learning-powered computational nutrient sensing system that appears to be feasible (validated accuracy against gold-standard weighed food method, positive end user engagement) and may provide a novel means for more accurate and objective tracking of LTC residents' food intake to support and prevent malnutrition tracking strategies.

Assessing jugular venous compliance with optical hemodynamic imaging by modulating intrathoracic pressure.

Significance: The internal jugular veins (IJV) are critical cerebral venous drainage pathways that are affected by right heart function. Cardiovascular disease and microgravity can alter central venous pressure (CVP) and venous return, which may contribute to increased intracranial pressure and decreased cardiac output. Assessing jugular venous compliance may provide insight into cerebral drainage and right heart function, but monitoring changes in vessel volume is challenging. Aim: We investigated the feasibility of quantifying jugular venous compliance from jugular venous attenuation (JVA), a noncontact optical measurement of blood volume, along with CVP from antecubital vein cannulation. Approach: CVP was progressively increased through a guided graded Valsalva maneuver, increasing mouth pressure by 2 mmHg every 2 s until a maximum expiratory pressure of 20 mmHg. JVA was extracted from a 1-cm segment between the clavicle and midneck. The contralateral IJV cross-sectional area (CSA) was measured with ultrasound to validate changes in the vessel size. Compliance was calculated using both JVA and CSA between four-beat averages over the duration of the maneuver. Results: JVA and CSA were strongly correlated (median and interquartile range) over the Valsalva maneuver across participants (r = 0.986, [0.983, 0.987]). CVP more than doubled on average between baseline and peak strain (10.7 ± 4.4 vs. 25.8 ± 5.4 cmH2O; p < 0.01). JVA and CSA increased nonlinearly with CVP, and both JVA- and CSA-derived compliance decreased progressively from baseline to peak strain (49% and 56% median reduction, respectively), with no significant difference in compliance reduction between the two measures (Z = - 1.24, p = 0.21). Pressure-volume curves showed a logarithmic relationship in both CSA and JVA. Conclusions: Optical jugular vein assessment may provide new ways to assess jugular distention and cardiac function.

Sex-dependent jugular vein optical attenuation and distension during head-down tilt and lower body negative pressure.

Non-contact coded hemodynamic imaging (CHI) is a novel wide-field near-infrared spectroscopy system which monitors blood volume by quantifying attenuation of light passing through the underlying vessels. This study tested the hypothesis that CHI-based jugular venous attenuation (JVA) would be larger in men, and change in JVA would be greater in men compared to women during two fluid shift challenges. The association of JVA with ultrasound-based cross-sectional area (CSA) was also tested. Ten men and 10 women completed three levels of head-down tilt (HDT) and four levels of lower body negative pressure (LBNP). Both JVA and CSA were increased by HDT and reduced by LBNP (all p < 0.001). Main effects of sex indicated that JVA was higher in men than women during both HDT (p = 0.003) and LBNP (p = 0.011). Interaction effects of sex and condition were observed for JVA during HDT (p = 0.005) and LBNP (p < 0.001). We observed moderate repeated-measures correlations (rrm ) between JVA and CSA in women during HDT (rrm  = 0.57, p = 0.011) and in both men (rrm  = 0.74, p < 0.001) and women (rrm  = 0.66, p < 0.001) during LBNP. While median within-person correlation coefficients indicated an even stronger association between JVA and CSA, this association became unreliable for small changes in CSA. As hypothesized, JVA was greater and changed more in men compared to women during both HDT and LBNP. CHI provides a non-contact method of tracking large changes in internal jugular vein blood volume that occur with acute fluid shifts, but data should be interpreted in a sex-dependent manner.

Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.

Malnutrition is a multidomain problem affecting 54% of older adults in long-term care (LTC). Monitoring nutritional intake in LTC is laborious and subjective, limiting clinical inference capabilities. Recent advances in automatic image-based food estimation have not yet been evaluated in LTC settings. Here, we describe a fully automatic imaging system for quantifying food intake. We propose a novel deep convolutional encoder-decoder food network with depth-refinement (EDFN-D) using an RGB-D camera for quantifying a plate's remaining food volume relative to reference portions in whole and modified texture foods. We trained and validated the network on the pre-labelled UNIMIB2016 food dataset and tested on our two novel LTC-inspired plate datasets (689 plate images, 36 unique foods). EDFN-D performed comparably to depth-refined graph cut on IOU (0.879 vs. 0.887), with intake errors well below typical 50% (mean percent intake error: [Formula: see text]%). We identify how standard segmentation metrics are insufficient due to visual-volume discordance, and include volume disparity analysis to facilitate system trust. This system provides improved transparency, approximates human assessors with enhanced objectivity, accuracy, and precision while avoiding hefty semi-automatic method time requirements. This may help address short-comings currently limiting utility of automated early malnutrition detection in resource-constrained LTC and hospital settings.

Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities.

Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min-1, [-262, 218]), spanning transitions from low-moderate (-23 ml min-1, [-250, 204]), low-high (14 ml min-1, [-252, 280]), ventilatory threshold-high (-49 ml min-1, [-274, 176]), and maximal (-32 ml min-1, [-261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted [Formula: see text] was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.

Multi-modal diffuse optical spectroscopy for high-speed monitoring and wide-area mapping of tissue optical properties and hemodynamics.

SIGNIFICANCE: Diffuse optical spectroscopic imaging (DOSI) is a versatile technology sensitive to changes in tissue composition and hemodynamics and has been used for a wide variety of clinical applications. Specific applications have prompted the development of versions of the DOSI technology to fit specific clinical needs. This work describes the development and characterization of a multi-modal DOSI (MM-DOSI) system that can acquire metabolic, compositional, and pulsatile information at multiple penetration depths in a single hardware platform. Additionally, a 3D tracking system is integrated with MM-DOSI, which enables registration of the acquired data to the physical imaging area. AIM: We demonstrate imaging, layered compositional analysis, and metabolism tracking capabilities using a single MM-DOSI system on optical phantoms as well as in vivo human tissue. APPROACH: We characterize system performance with a silicone phantom containing an embedded object. To demonstrate multi-layer sensitivity, we imaged human calf tissue with a 4.8-mm skin-adipose thickness. Human thenar tissue was also measured using a combined broadband DOSI and continuous-wave near-infrared spectroscopy method (∼15 Hz acquisition rate). RESULTS: High-resolution optical property maps of absorption (µa) and reduced scattering (µs ' ) were recovered on the phantom by capturing over 1000 measurement points in under 5 minutes. On human calf tissue, we show two probing depth layers have significantly different (p < 0.001) total-hemo/myoglobin and µs ' composition. On thenar tissue, we calculate tissue arterial oxygen saturation, venous oxygen saturation, and tissue metabolic rate of oxygen consumption during baseline and after release of an arterial occlusion. CONCLUSIONS: The MM-DOSI can switch between collection of broadband spectra, high-resolution images, or multi-depth hemodynamics without any hardware reconfiguration. We conclude that MM-DOSI enables acquisition of high resolution, multi-modal data consolidated in a single platform, which can provide a more comprehensive understanding of tissue hemodynamics and composition for a wide range of clinical applications.

Real-Time Handheld Probe Tracking and Image Formation Using Digital Frequency-Domain Diffuse Optical Spectroscopy.

OBJECTIVE: Frequency-domain diffuse optical spectroscopic imaging (FD-DOS) is a non-invasive method for measuring absolute concentrations of tissue chromophores such as oxy- and deoxy-hemoglobin in vivo. The utility of FD-DOS for clinical applications such as monitoring chemotherapy response in breast cancer has previously been demonstrated, but challenges for further clinical translation, such as slow acquisition speed and lack of user feedback, remain. Here, we propose a new high speed FD-DOS instrument that allows users to freely acquire measurements over the tissue surface, and is capable of rapidly imaging large volumes of tissue. METHODS: We utilize 3D monocular probe tracking combined with custom digital FD-DOS hardware and a high-speed data processing pipeline for the instrument. Results are displayed during scanning over the surface of the sample using a probabilistic Monte Carlo light propagation model. RESULTS: We show this instrument can measure absorption and scattering coefficients with an error of 7% and 1% respectively, with 0.7 mm positional accuracy. We demonstrate the equivalence of our visualization methodology with a standard interpolation approach, and demonstrate two proof-of-concept in vivo results showing superficial vasculature in the human forearm and surface contrast in a healthy human breast. CONCLUSION: Our new FD-DOS system is able to compute chromophore concentrations in real-time (1.5 Hz) in vivo. SIGNIFICANCE: This method has the potential to improve the quality of FD-DOS image scans while reducing measurement times for a variety of clinical applications.

Optical Hemodynamic Imaging of Jugular Venous Dynamics During Altered Central Venous Pressure.

OBJECTIVE: An optical imaging system is proposed for quantitatively assessing jugular venous response to altered central venous pressure. METHODS: The proposed system assesses sub-surface optical absorption changes from jugular venous waveforms with a spatial calibration procedure to normalize incident tissue illumination. Widefield frames of the right lateral neck were captured and calibrated using a novel flexible surface calibration method. A hemodynamic optical model was derived to quantify jugular venous optical attenuation (JVA) signals, and generate a spatial jugular venous pulsatility map. JVA was assessed in three cardiovascular protocols that altered central venous pressure: acute central hypovolemia (lower body negative pressure), venous congestion (head-down tilt), and impaired cardiac filling (Valsalva maneuver). RESULTS: JVA waveforms exhibited biphasic wave properties consistent with jugular venous pulse dynamics when time-aligned with an electrocardiogram. JVA correlated strongly (median, interquartile range) with invasive central venous pressure during graded central hypovolemia (r = 0.85, [0.72, 0.95]), graded venous congestion (r = 0.94, [0.84, 0.99]), and impaired cardiac filling (r = 0.94, [0.85, 0.99]). Reduced JVA during graded acute hypovolemia was strongly correlated with reductions in stroke volume (SV) (r = 0.85, [0.76, 0.92]) from baseline (SV: 79 ± 15 mL, JVA: 0.56 ± 0.10 a.u.) to -40 mmHg suction (SV: 59 ± 18 mL, JVA: 0.47 ± 0.05 a.u.; p 0.01). CONCLUSION: The proposed non-contact optical imaging system demonstrated jugular venous dynamics consistent with invasive central venous monitoring during three protocols that altered central venous pressure. SIGNIFICANCE: This system provides non-invasive monitoring of pressure-induced jugular venous dynamics in clinically relevant conditions where catheterization is traditionally required, enabling monitoring in non-surgical environments.

Monocular 3D Sway Tracking for Assessing Postural Instability in Cerebral Hypoperfusion During Quiet Standing.

Postural instability is prevalent in aging and neurodegenerative disease, decreasing quality of life and independence. Quantitatively monitoring balance control is important for assessing treatment efficacy and rehabilitation progress. However, existing technologies for assessing postural sway are complex and expensive, limiting their widespread utility. Here, we propose a monocular imaging system capable of assessing sub-millimeter 3D sway dynamics during quiet standing. Two anatomical targets with known feature geometries were placed on the lumbar and shoulder. Upper and lower trunk 3D kinematic motion were automatically assessed from a set of 2D frames through geometric feature tracking and an inverse motion model. Sway was tracked in 3D and compared between control and hypoperfusion conditions in 14 healthy young adults. The proposed system demonstrated high agreement with a commercial motion capture system (error [Formula: see text], [-0.52, 0.52]). Between-condition differences in sway dynamics were observed in anterior-posterior sway during early and mid stance, and medial-lateral sway during mid stance commensurate with decreased cerebral perfusion, followed by recovered sway dynamics during late stance with cerebral perfusion recovery. This inexpensive single-camera system enables quantitative 3D sway monitoring for assessing neuromuscular balance control in weakly constrained environments.

Monocular 3D Probe Tracking for Generating Sub-Surface Optical Property Maps From Diffuse Optical Spectroscopic Imaging.

OBJECTIVE: Diffuse optical spectroscopic imaging (DOSI) is a promising biophotonic technology for clinical tissue assessment, but is currently hampered by difficult wide area assessment. A co-integrative optical imaging system is proposed for dense sub-surface optical property spatial assessment. METHODS: The proposed system fuses a co-aligned set of camera frames and diffuse optical spectroscopy measurements to generate spatial sub-surface optical property maps. A 3D rigid body motion estimation model was developed by fitting automatically detected target features to an a priori geometric model using a single overhead camera. Point-wise optical properties were measured across the tissue using frequency domain photon migration DOSI. The 3D probe trajectory and temporal optical property data were fused to generate 2D spatial optical property maps, which were projected onto the tissue image using pre-calibrated camera parameters. RESULTS: The system demonstrated sub-millimeter positional accuracy (error 0.24 ± 0.35 mm) across different probe speeds (1.0-3.8 cm/s), and displacement accuracy in overhead ([Formula: see text] mm) and tilted (0.51 ± 0.51 mm) camera orientations. Unstructured scans on a tumor inclusion phantom showed strong contrast under different probe paths, and significant ( ) changes in optical properties in an in vivo leg cuff occlusion protocol with spatial anatomy localization. CONCLUSION: The proposed co-integrative optical imaging system generated dense sub-surface optical property distributions across wide tissue areas with sub-millimeter accuracy at different probe speeds and trajectories, and does not require pre-planned probe route for tissue assessment. SIGNIFICANCE: This system provides a valuable tool for real-time non-invasive tissue health and cancer screening, and enables longitudinal disease progression assessment through unstructured probe-based optical tissue assessment.

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

Physical activity levels are related through algorithms to the energetic demand, with no information regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o2) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇o2 kinetics). This study evaluated aerobic system dynamics based on predicted V̇o2 data obtained from wearable sensors during unsupervised activities of daily living (µADL). Thirteen healthy men performed a laboratory-controlled moderate exercise protocol and were monitored for ≈6 h/day for 4 days (µADL data). Variables derived from hip accelerometer (ACCHIP), heart rate monitor, and respiratory bands during µADL were extracted and processed by a validated random forest regression model to predict V̇o2. The aerobic system analysis was based on the frequency-domain analysis of ACCHIP and predicted V̇o2 data obtained during µADL. Optimal samples for frequency domain analysis (constrained to ≤0.01 Hz) were selected when ACCHIP was higher than 0.05 g at a given frequency (i.e., participants were active). The temporal characteristics of predicted V̇o2 data during µADL correlated with the temporal characteristics of measured V̇o2 data during laboratory-controlled protocol ([Formula: see text] = 0.82, P < 0.001, n = 13). In conclusion, aerobic system dynamics can be investigated during unsupervised activities of daily living by wearable sensors. Although speculative, these algorithms have the potential to be incorporated into wearable systems for early detection of changes in health status in realistic environments by detecting changes in aerobic response dynamics. NEW & NOTEWORTHY The early detection of subclinical aerobic system impairments might be indicative of impaired physiological reserves that impact the capacity for physical activity. This study is the first to use wearable sensors in unsupervised activities of daily living in combination with novel machine learning algorithms to investigate the aerobic system dynamics with the potential to contribute to models of functional health status and guide future individualized health care in the normal population.

Non-contact hemodynamic imaging reveals the jugular venous pulse waveform.

Cardiovascular monitoring is important to prevent diseases from progressing. The jugular venous pulse (JVP) waveform offers important clinical information about cardiac health, but is not routinely examined due to its invasive catheterisation procedure. Here, we demonstrate for the first time that the JVP can be consistently observed in a non-contact manner using a photoplethysmographic imaging system. The observed jugular waveform was strongly negatively correlated to the arterial waveform (r = -0.73 ± 0.17), consistent with ultrasound findings. Pulsatile venous flow was observed over a spatially cohesive region of the neck. Critical inflection points (c, x, v, y waves) of the JVP were observed across all participants. The anatomical locations of the strongest pulsatile venous flow were consistent with major venous pathways identified through ultrasound.

Spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging.

Photoplethysmographic imaging is an optical solution for non-contact cardiovascular monitoring from a distance. This camera-based technology enables physiological monitoring in situations where contact-based devices may be problematic or infeasible, such as ambulatory, sleep, and multi-individual monitoring. However, automatically extracting the blood pulse waveform signal is challenging due to the unknown mixture of relevant (pulsatile) and irrelevant pixels in the scene. Here, we propose a signal fusion framework, FusionPPG, for extracting a blood pulse waveform signal with strong temporal fidelity from a scene without requiring anatomical priors. The extraction problem is posed as a Bayesian least squares fusion problem, and solved using a novel probabilistic pulsatility model that incorporates both physiologically derived spectral and spatial waveform priors to identify pulsatility characteristics in the scene. Evaluation was performed on a 24-participant sample with various ages (9-60 years) and body compositions (fat% 30.0 ± 7.9, muscle% 40.4 ± 5.3, BMI 25.5 ± 5.2 kg·m-2). Experimental results show stronger matching to the ground-truth blood pulse waveform signal compared to the FaceMeanPPG (p < 0.001) and DistancePPG (p < 0.001) methods. Heart rates predicted using FusionPPG correlated strongly with ground truth measurements (r2 = 0.9952). A cardiac arrhythmia was visually identified in FusionPPG's waveform via temporal analysis.

Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems.

Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1??kg·m?2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen­Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,p<0.01) and spectral SNR (W=31,p<0.01) compared to uniform spatial averaging. Heart rate estimation was in strong agreement with true heart rate [r2=0.9619, error (?,?)=(0.52,1.69) bpm].

Estimating oxygen uptake and energy expenditure during treadmill walking by neural network analysis of easy-to-obtain inputs.

The study of oxygen uptake (V̇o2) dynamics during walking exercise transitions adds valuable information regarding fitness. However, direct V̇o2 measurements are not practical for general population under realistic settings. Devices to measure V̇o2 are associated with elevated cost, uncomfortable use of a mask, need of trained technicians, and impossibility of long-term data collection. The objective of this study was to predict the V̇o2 dynamics from heart rate and inputs from the treadmill ergometer by a novel artificial neural network approach. To accomplish this, 10 healthy young participants performed one incremental and three moderate constant work rate treadmill walking exercises. The speed and grade used for the moderate-intensity protocol was related to 80% of the V̇o2 response at the gas exchange threshold estimated during the incremental exercise. The measured V̇o2 was used to train an artificial neural network to create an algorithm able to predict the V̇o2 based on easy-to-obtain inputs. The dynamics of the V̇o2 response during exercise transition were evaluated by exponential modeling. Within each participant, the predicted V̇o2 was strongly correlated to the measured V̇o2 ( = 0.97 ± 0.0) and presented a low bias (~0.2%), enabling the characterization of the V̇o2 dynamics during treadmill walking exercise. The proposed algorithm could be incorporated into smart devices and fitness equipment, making them suitable for tracking changes in aerobic fitness and physical health beyond the infrequent monitoring of patients during clinical interventions and rehabilitation programs.

Feasibility of long-distance heart rate monitoring using transmittance photoplethysmographic imaging (PPGI).

Photoplethysmography (PPG) devices are widely used for monitoring cardiovascular function. However, these devices require skin contact, which restricts their use to at-rest short-term monitoring. Photoplethysmographic imaging (PPGI) has been recently proposed as a non-contact monitoring alternative by measuring blood pulse signals across a spatial region of interest. Existing systems operate in reflectance mode, many of which are limited to short-distance monitoring and are prone to temporal changes in ambient illumination. This paper is the first study to investigate the feasibility of long-distance non-contact cardiovascular monitoring at the supermeter level using transmittance PPGI. For this purpose, a novel PPGI system was designed at the hardware and software level. Temporally coded illumination (TCI) is proposed for ambient correction, and a signal processing pipeline is proposed for PPGI signal extraction. Experimental results show that the processing steps yielded a substantially more pulsatile PPGI signal than the raw acquired signal, resulting in statistically significant increases in correlation to ground-truth PPG in both short- and long-distance monitoring. The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

Physiological characterization of skin lesion using non-linear random forest regression model.

The current diagnostic technique for melanoma solely relies on the surface level of skin and under-skin information is neglected. Since physiological features of skin such as melanin are closely related to development of melanoma, the non-linear physiological feature extraction model based on random forest regression is proposed. The proposed model characterizes the concentration of eumelanin and pheomelanin from standard camera images or dermoscopic images, which are conventionally used for diagnosis of melanoma. For the validation, the phantom study and the separability test using clinical images were conducted and compared against the state-of-the art non-linear and linear feature extraction models. The results showed that the proposed model outperformed other comparing models in phantom and clinical experiments. Promising results show that the quantitative characterization of skin features, which is provided by the proposed method, can allow dermatologists and clinicians to make a more accurate and improved diagnosis of melanoma.