Hybrid Nanostructured Textile Bioelectrode for Unobtrusive Health Monitoring

Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made with the skin based on the fundamental skin impedance model and electromagnetic theory. The hybrid nanostructured dry electrodes provided large surface area and better contact with skin that improved electrode sensitivity and reduced the effect of changing skin properties, which are the problems usually faced by conventional dry textile electrodes. The dry electrodes can only register strong physiological signals because of high background noise levels, thus limiting the use of existing dry electrodes to heart rate measurement and respiration. Therefore, dry electrode systems cannot be used for recording complete ECG waveform, EEG or measurement of bioimpedance. Because of their improved sensitivity these hybrid nanostructured dry electrodes can be applied to measurement of ECG and bioimpedance with very low baseline noise. These textile based electrodes can be seamlessly integrated into garments of daily use such as vests and bra. In combination with embedded wireless network device that can communicate with smart phone, laptop or GPRS, they can function as wearable wireless health diagnostic systems

Bootstrap Variance Estimation Technique under Dual Frame Ranked Set Sampling

Not AvailableMultiple frames are preferably used when a satisfactory sampling frame, covering the whole population in question, is unavailable or even if such a frame is available it may not be economically advantageous to use that frame for survey because of high cost of sampling per unit. In this paper, we dealt with the problem of variance estimation of the dual frame ranked set sample (DFRSS) estimator. We propose two rescaling Bootstrap variance estimation methods viz. strata based and cluster based, to obtain an unbiased estimator of the sampling variance of the proposed estimator. The comparison of performance of the proposed rescaled bootstrap methods with standard bootstrap methods were investigated through a simulation study. The simulation results show that the proposed methods are more stable and have lesser relative bias than the standard approaches. Among the two rescaling Bootstrap variance estimation methods, the strata based rescaling Bootstrap variance estimation approach is more powerful than its counterpart.Not Availabl

Wearable Wireless Cardiovascular Monitoring Using Textile-Based Nanosensor and Nanomaterial Systems

Wearable and ultraportable electronics coupled with pervasive computing are poised to revolutionize healthcare services delivery. The potential cost savings in both treatment, as well as preventive care are the focus of several research efforts across the globe. In this review, we describe the motivations behind wearable solutions to real-time cardiovascular monitoring from a perspective of current healthcare services, as well as from a systems design perspective. We identify areas where emerging research is underway, namely: nanotechnology in textile-based wearable monitors and healthcare solutions targeted towards smart devices, like smartphones and tablets

Time-Deniable Signatures

In this work we propose time-deniable signatures (TDS), a new primitive that facilitates deniable authentication in protocols such as DKIM-signed email. As with traditional signatures, TDS provide strong authenticity for message content, at least for a sender-chosen period of time. Once this time period has elapsed, however, time-deniable signatures can be forged by any party who obtains a signature. This forgery property ensures that signatures serve a useful authentication purpose for a bounded time period, while also allowing signers to plausibly disavow the creation of older signed content. Most critically, and unlike many past proposals for deniable authentication, TDS do not require interaction with the receiver or the deployment of any persistent cryptographic infrastructure or services beyond the signing process (e.g., APIs to publish secrets or author timestamp certificates.) We first investigate the security definitions for time-deniability, demonstrating that past definitional attempts are insufficient (and indeed, allow for broken signature schemes.) We then propose an efficient construction of TDS based on well-studied assumptions

Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs

Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6

Background and Objective: The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Methods: Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. Results: ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Conclusions: Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs