Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis.

Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.

BCG Signal Quality Assessment Based on Time-Series Imaging Methods.

This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts. This distortion leads to errors in blood pressure estimation, thereby lowering the performance of blood pressure measurement based on BCG. In this study, to prevent such performance degradation, a binary classification model was created to distinguish between high-quality versus low-quality BCG signals. To estimate the most accurate model, four time-series imaging methods (recurrence plot, the Gramain angular summation field, the Gramain angular difference field, and the Markov transition field) were studied to convert the temporal BCG signal associated with each heartbeat into a 448 × 448 pixel image, and the image was classified using CNN models such as ResNet, SqueezeNet, DenseNet, and LeNet. A total of 9626 BCG beats were used for training, validation, and testing. The experimental results showed that the ResNet and SqueezeNet models with the Gramain angular difference field method achieved a binary classification accuracy of up to 87.5%.

Posture-Dependent Variability in Wrist Ballistocardiogram-Photoplethysmogram Pulse Transit Time: Implication to Cuff-Less Blood Pressure Tracking.

OBJECTIVE: Toward the ultimate goal of robust cuff-less blood pressure (BP) tracking with wrist wearables against postural changes, the goal of this work was to investigate posture-dependent variability in pulse transit time (PTT) measured with ballistocardiogram (BCG) and photoplethysmogram (PPG) signal pair at the wrist. METHODS: BCG and PPG signals were acquired from 25 subjects under the combination of 3 body (standing, sitting, and supine) and 3 arm (vertical in head-to-foot direction, placed on the chest, and holding a shoulder) postures. PTT was computed as the time interval between the BCG J wave and the PPG foot, and the impact of the 9 postures on PTT was analyzed by invoking an array of possible physical mechanisms. RESULTS: Our work suggests that (i) wrist BCG-PPG PTT is consistent under standing and sitting postures with vertically held arms; and (ii) changes in wrist orientation and height as well as restrictions in body and arm movement may alter wrist BCG-PPG PTT via distortions in the wrist BCG and PPG waveforms. The results indicate that wrist BCG-PPG PTT varies with respect to postures even when BP remains constant. CONCLUSION: The potential of cuff-less BP tracking via wrist BCG-PPG PTT demonstrated under standing posture with arms vertically down in the head-to-foot direction may not generalize to other body and arm postures. SIGNIFICANCE: Understanding the physical mechanisms responsible for posture-induced BCG-PPG PTT variability may increase the versatility of the wrist BCG for cuff-less BP tracking.

Classification of Parkinson's disease with freezing of gait based on 360° turning analysis using 36 kinematic features.

BACKGROUND: Freezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression. METHODS: The study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression. RESULTS: In feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification. CONCLUSION: We suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

Accurate Single-Cell Clustering through Ensemble Similarity Learning.

Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger number of artificial zeros, and the technical noise makes it difficult to develop effective single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm that can accurately predict single-cell clusters in large-scale single-cell sequencing by effectively reducing the zero-inflated noise and accurately estimating the cell-to-cell similarities. First, we construct an ensemble similarity network based on different similarity estimates, and reduce the artificial noise using a random walk with restart framework. Finally, starting from a larger number small size but highly consistent clusters, we iteratively merge a pair of clusters with the maximum similarities until it reaches the predicted number of clusters. Extensive performance evaluation shows that the proposed single-cell clustering algorithm can yield the accurate single-cell clustering results and it can help deciphering the key messages underlying complex biological mechanisms.

Hand Gesture Recognition Using EGaIn-Silicone Soft Sensors.

Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).

A Unified Approach to Wearable Ballistocardiogram Gating and Wave Localization.

OBJECTIVE: Toward the ultimate goal of cuff-less blood pressure (BP) trend tracking via pulse transit time (PTT) using wearable ballistocardiogram (BCG) signals, we present a unified approach to the gating of wearable BCG and the localization of wearable BCG waves. METHODS: We present a unified approach to localize wearable BCG waves suited to various gating and localization reference signals. Our approach gates individual wearable BCG beats and identifies candidate waves in each wearable BCG beat using a fiducial point in a reference signal, and exploits a pre-specified probability distribution of the time interval between the BCG wave and the fiducial point in the reference signal to accurately localize the wave in each wearable BCG beat. We tested the validity of our approach using experimental data collected from 17 healthy volunteers. RESULTS: We showed that our approach could localize the J wave in the wearable wrist BCG accurately with both the electrocardiogram (ECG) and the wearable wrist photoplethysmogram (PPG) signals as reference, and that the wrist BCG-PPG PTT thus derived exhibited high correlation to BP. CONCLUSION: We demonstrated the proof-of-concept of a unified approach to localize wearable BCG waves suited to various gating and localization reference signals compatible with wearable measurement. SIGNIFICANCE: Prior work using the BCG itself or the ECG to gate the BCG beats and localize the waves to compute PTT are not ideally suited to the wearable BCG. Our approach may foster the development of cuff-less BP monitoring technologies based on the wearable BCG.

The Potential of Wearable Limb Ballistocardiogram in Blood Pressure Monitoring via Pulse Transit Time.

The goal of this study was to investigate the potential of wearable limb ballistocardiography (BCG) to enable cuff-less blood pressure (BP) monitoring, by investigating the association between wearable limb BCG-based pulse transit time (PTT) and BP. A wearable BCG-based PTT was calculated using the BCG and photoplethysmogram (PPG) signals acquired by a wristband as proximal and distal timing reference (called the wrist PTT). Its efficacy as surrogate of BP was examined in comparison with PTT calculated using the whole-body BCG acquired by a customized weighing scale (scale PTT) as well as pulse arrival time (PAT) using the experimental data collected from 22 young healthy participants under multiple BP-perturbing interventions. The wrist PTT exhibited close association with both diastolic (group average r = 0.79; mean absolute error (MAE) = 5.1 mmHg) and systolic (group average r = 0.81; MAE = 7.6 mmHg) BP. The efficacy of the wrist PTT was superior to scale PTT and PAT for both diastolic and systolic BP. The association was consistent and robust against diverse BP-perturbing interventions. The wrist PTT showed superior association with BP when calculated with green PPG rather than infrared PPG. In sum, wearable limb BCG has the potential to realize convenient cuff-less BP monitoring via PTT.

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography.

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Physiological Association between Limb Ballistocardiogram and Arterial Blood Pressure Waveforms: A Mathematical Model-Based Analysis.

By virtue of its direct association with the cardiovascular (CV) functions and compatibility to unobtrusive measurement during daily activities, the limb ballistocardiogram (BCG) is receiving an increasing interest as a viable means for ultra-convenient CV health and disease monitoring. However, limited insights on its physical implications have hampered disciplined interpretation of the BCG and systematic development of the BCG-based approaches for CV health monitoring. In this study, a mathematical model that can predict the limb BCG in responses to the arterial blood pressure (BP) waves in the aorta was developed and experimentally validated. The validated mathematical model suggests that (i) the limb BCG waveform reveals the timings and amplitudes associated with the aortic BP waves; (ii) mechanical filtering exerted by the musculoskeletal properties of the body can obscure the manifestation of the arterial BP waves in the limb BCG; and (iii) the limb BCG exhibits meaningful morphological changes in response to the alterations in the CV risk predictors. The physical insights garnered by the analysis of the mathematical model may open up new opportunities toward next generation of the BCG-based CV healthcare techniques embedded with transparency, interpretability, and robustness against the external variability.

Forecasting Hypotension during Vasopressor Infusion via Time Series Analysis.

For optimal management of hypotension during continuous vasopressor infusion, this study investigated two forecasting models, logistic regression (LR) and auto-regressive (AR) models, to predict sustained hypotension episodes (SHEs) in the ICU, before the SHE occurred. Two investigational models were compared to a simple threshold detector, which alerts whenever the BP is less than the specific hypotension threshold. Datasets were collected from 207 patients treated for a variety of clinical indications in two different hospitals (Hospital 1 & 2). For the 60 mmHg hypotension threshold, LR model predicted SHEs an average of 7.0 min before (Hospital 1) and 2.5 min before (Hospital 2), and the AR model predicted SHEs 10.5 min and 2.0 min before (Hospital 1 and 2 respectively). Both were significantly better than the threshold method and without higher false alarm rates. The AR model offered the flexibility to predict for different hypotension thresholds.

Data mining investigation of the association between a limb ballistocardiogram and blood pressure.

OBJECTIVE: To investigate the association between a limb ballistocardiogram (BCG) and blood pressure (BP) based on data mining. APPROACH: During four BP-perturbing interventions, the BCG and reference BP were measured from 23 young, healthy volunteers using a custom-manufactured wristband equipped with a MEMS accelerometer and a commercial continuous BP measurement device. Both timing and amplitude features in the wrist BCG waveform were extracted, and significant features predictive of diastolic (DP) and systolic (SP) BP were selected using stepwise linear regression analysis. The selected features were further compressed using principal component analysis to yield a small set of DP and SP predictors. The association between the predictors thus obtained and BP was investigated by multivariate linear regression analysis. MAIN RESULTS: The predictors exhibited a meaningful association with BP. When three most significant predictors were used for DP and SP, a correlation coefficient of r = 0.75 ± 0.03 (DP) and r = 0.75 ± 0.03 (SP), a root-mean-squared error (RMSE) of 7.4 ± 0.6 mmHg (DP) and 10.3 ± 0.8 mmHg (SP), and a mean absolute error (MAE) of 6.0 ± 0.5 mmHg (DP) and 8.3 ± 0.7 mmHg (SP) were obtained across all interventions (mean ± SE). The association was consistent in all the individual interventions (r ⩾ 0.68, RMSE ⩽ 5.7 mmHg, and MAE ⩽ 4.5 mmHg for DP as well as r ⩾ 0.61, RMSE ⩽ 7.9 mmHg, and MAE ⩽ 6.4 mmHg for SP on the average). The minimum number of requisite predictors for robust yet practically realistic BP monitoring appeared to be three. The association between predictors and BP was maintained even under regularized calibration (r = 0.63 ± 0.05, RMSE = 9.3 ± 0.8 mmHg, and MAE = 7.6 ± 0.7 mmHg for DP as well as r = 0.60 ± 0.05, RMSE = 14.7 ± 1.4 mmHg, and MAE = 11.9 ± 1.1 mmHg for SP (mean ± SE)). The requisite predictors for DP and SP were distinct from each other. SIGNIFICANCE: The results of this study may provide a viable basis for ultra-convenient BP monitoring based on a limb BCG alone.

Hypotension in ICU Patients Receiving Vasopressor Therapy.

Vasopressor infusion (VPI) is used to treat hypotension in an ICU. We studied compliance with blood pressure (BP) goals during VPI and whether a statistical model might be efficacious for advance warning of impending hypotension, compared with a basic hypotension threshold alert. Retrospective data were obtained from a public database. Studying adult ICU patients receiving VPI at submaximal dosages, we analyzed characteristics of sustained hypotension episodes (>15 min) and then developed a logistic regression model to predict hypotension episodes using input features related to BP trends. The model was then validated with prospective data. In the retrospective dataset, 102-of-215 ICU stays experienced >1 hypotension episode (median of 2.5 episodes per day in this subgroup). When trained with 75% of retrospective dataset, testing with the remaining 25% of the dataset showed that the model and the threshold alert detected 99.6% and 100% of the episodes, respectively, with median advance forecast times (AFT) of 12 and 0 min. In a second, prospective dataset, the model detected 100% of 26 episodes with a median AFT of 22 min. In conclusion, episodes of hypotension were common during VPI in the ICU. A logistic regression model using BP temporal trend features predicted the episodes before their onset.

Domain walls and anchoring transitions mimicking nematic biaxiality in the oxadiazole bent-core liquid crystal C7.

We investigate the origin of "secondary disclinations" that were recently described as new evidence of a biaxial nematic phase in an oxadiazole bent-core thermotropic liquid crystal C7. Using an assortment of optical techniques such as polarizing optical microscopy, LC PolScope, and fluorescence confocal polarizing microscopy, we demonstrate that the secondary disclinations represent non-singular domain walls formed in a uniaxial nematic phase during the surface anchoring transition, in which surface orientation of the director changes from tangential (parallel to the bounding plates) to tilted. Each domain wall separates two regions with the director tilted in opposite azimuthal directions. At the centre of the wall, the director remains parallel to the bounding plates. The domain walls can be easily removed by applying a moderate electric field. The anchoring transition is explained by the balance of (a) the intrinsic perpendicular surface anchoring produced by the polyimide aligning layer and (b) tangential alignment caused by ionic impurities forming electric double layers. The model is supported by the fact that the temperature of the tangentially tilted anchoring transition decreases as the cell thickness increases and as the concentration of ionic species (added salt) increases. We also demonstrate that the surface alignment is strongly affected by thermal degradation of the samples. This study shows that C7 exhibits only a uniaxial nematic phase and demonstrates yet another mechanism (formation of "secondary disclinations") by which a uniaxial nematic phase can mimic a biaxial nematic behaviour.

Surface alignment, anchoring transitions, optical properties, and topological defects in the thermotropic nematic phase of organo-siloxane tetrapodes.

We perform optical, surface anchoring, and textural studies of an organo-siloxane "tetrapode" material in the broad temperature range of the nematic phase. The optical, structural, and topological features are compatible with the uniaxial nematic order rather than with the biaxial nematic order, in the entire nematic temperature range -25 °C < T < 46 °C studied. For homeotropic alignment, the material experiences surface anchoring transition, but the director can be realigned into an optically uniaxial texture by applying a sufficiently strong electric field. The topological features of textures in cylindrical capillaries, in spherical droplets and around colloidal inclusions are consistent with the uniaxial character of the long-range nematic order. In particular, we observe isolated surface point defects - boojums and bulk point defects - hedgehogs that can exist only in the uniaxial nematic liquid crystal.

In-plane switching of vertically aligned negative liquid crystals for high transmittance and wide viewing angle.

We propose a method for in-plane switching of vertically aligned negative liquid crystals (LCs) for high transmittance and wide viewing angle. By applying an in-plane electric field using a double-layered electrode structure, LC molecules can be rotated by the vertical as well as the in-plane components of the applied field over the entire region so that high transmittance can be achieved. The threshold voltage difference can be obtained simply by varying the electrode structure, which can reduce the off-axis gamma shift in a multidomain vertical alignment LC cell.