Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions.

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed.

1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms.

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation - when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor of 194 subjects.

Data Compression via Low Complexity Delta Transition Lossless Encoding for Remote Physiological and Environmental Monitoring.

Continuous remote physiologic and environmental monitoring, employing an ever-increasing array of sensors, is now commonplace. Given the significant amount of data being digitized, two common sources of energy consumption can be targeted to improve device runtime: data storage and data transmission. One embedded method to maximize device runtime is inline low energy data compression.

Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation.

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli.

Simulated imaging of atherosclerotic & radiofrequency ablation lesions using phase subtraction.

Cardiovascular diseases are the main cause of death worldwide. Atherosclerosis and atrial fibrillation are structural and electrical pathophysiology, respectively, that can lead to acute events such as stroke or myocardial infarction. We used particle-based Monte Carlo methods to simulate X-ray phase imaging of atherosclerotic plaque types IV-VIII in the aorta, iliac, and coronary arteries.

Design of posture and activity detector (PAD).

In recent years, there has been much research and development of wearable devices using accelerometers for studying physical activity. Previously, we have described the development of the Posture and Activity Detector (PAD). After demonstrating success with PAD, we were motivated to improve the design by taking the device one step further and implementing all of these components on a single printed circuit board, adding a few additional features to make the system more flexible, and custom-designing an outer case.