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

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Reconstructing Electrocardiogram Leads From a Reduced Lead Set Through the Use of Patient-Specific Transforms and Independent Component Analysis

In this exploration into electrocardiogram (ECG) lead reconstruction, two algorithms were developed and tested on a public database and in real-time on patients. These algorithms were based on independent component analysis (ICA). ICA was a promising method due to its implications for spatial independence of lead placement and its adaptive nature to changing orientation of the heart in relation to the electrodes. The first algorithm was used to reconstruct missing precordial leads, which has two key applications. The first is correcting precordial lead measurements in a standard 12-lead configuration. If an irregular signal or high level of noise is detected on a precordial lead, the obfuscated signal can be calculated from other nearby leads. The second is the reduction in the number of precordial leads required for accurate measurement, which opens up the surface of the chest above the heart for diagnostic procedures. Using only two precordial leads, the other four were reconstructed with a high degree of accuracy. This research was presented at the 33rd International Conference of the IEEE Engineering in Medicine and Biology Society in 2011.1 The second algorithm was developed to construct a full 12-lead clinical ECG from either three differential measurements or three standard leads. By utilizing differential measurements, the ECG could be reconstructed using wireless systems, which lack the common ground necessary for the standard measurement method. Using three leads distributed across the expanse of the space of the heart, all twelve leads were successfully reconstructed and compared against state of the art algorithms. This work has been accepted for publication in the IEEE Journal of Biomedical and Health Informatics.2 These algorithms show a proof of concept, one which can be further honed to deal with the issues of sorting independent components and improving the training sequences. This research also revealed the possibility of extracting and monitoring additional physiological information, such as a patient\u27s breathing rate from currently utilized ECG systems

Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices

Improving accuracy of derived 12-lead electrocardiography by waveform segmentation

A number of methods have been proposed to reduce number of leads for electrocardiography (ECG) measurement without decreasing the signal quality. Some limited sets of leads that are nearly orthogonal, such as EASI, have been used to reconstruct the standard 12-lead ECG by various transformation techniques including linear, nonlinear, generic, and patient-specific. Those existing techniques, however, employed a full-cycle ECG waveform to calculate the transformation coefficients. Instead of calculating the transformation coefficients using a full-cycle waveform, we propose a new approach that segments the waveform into three segments: PR, QRS complex, and ST, hence the transformation coefficients were segment-specific. For testing, our new segment-specific approach was applied to six existing methods: Dower’s method with generic coefficients, Dower’s method with individual (patient-specific) coefficients, Linear Regression (LR), 2nd degree Polynomial Regression (PR), 3rd degree PR, and Artificial Neural Network (ANN). The results showed that the new approach outperformed the conventional full-cycle approach. It was able to significantly reduce the derivation error up to 74.50% as well as improve the correlation coefficient up to 0.66%

Cardiac electrical defects in progeroid mice and Hutchinson-Gilford progeria syndrome patients with nuclear lamina alterations

Hutchinson–Gilford progeria syndrome (HGPS) is a rare genetic disease caused by defective prelamin A processing, leading to nuclear lamina alterations, severe cardiovascular pathology, and premature death. Prelamin A alterations also occur in physiological aging. It remains unknown how defective prelamin A processing affects the cardiac rhythm. We show age-dependent cardiac repolarization abnormalities in HGPS patients that are also present in the Zmpste24-/- mouse model of HGPS. Challenge of Zmpste24-/- mice with the ß-adrenergic agonist isoproterenol did not trigger ventricular arrhythmia but caused bradycardia-related premature ventricular complexes and slow-rate polymorphic ventricular rhythms during recovery. Patch-clamping in Zmpste24-/- cardiomyocytes revealed prolonged calcium-transient duration and reduced sarcoplasmic reticulum calcium loading and release, consistent with the absence of isoproterenol-induced ventricular arrhythmia. Zmpste24-/- progeroid mice also developed severe fibrosis-unrelated bradycardia and PQ interval and QRS complex prolongation. These conduction defects were accompanied by overt mislocalization of the gap junction protein connexin43 (Cx43). Remarkably, Cx43 mislocalization was also evident in autopsied left ventricle tissue from HGPS patients, suggesting intercellular connectivity alterations at late stages of the disease. The similarities between HGPS patients and progeroid mice reported here strongly suggest that defective cardiac repolarization and cardiomyocyte connectivity are important abnormalities in the HGPS pathogenesis that increase the risk of arrhythmia and premature death.Peer ReviewedPostprint (published version

Personalized reduced 3-lead system formation methodology for Remote Health Monitoring applications and reconstruction of standard 12-lead system

Remote Health Monitoring (RHM) applications encounter limitations from technological front viz. bandwidth, storage and transmission time and the medical science front i.e. usage of 2-3 lead systems instead of the standard 12-lead (S12) system. Technological limitations constraint the number of leads to 2-3 while cardiologists accustomed with 12-Lead ECG may find these 2-3 lead systems insufficient for diagnosis. Thus, the aforementioned limitations pose self-contradicting challenges for RHM. A personalized reduced 2/3 lead system is required which can offer equivalent information as contained in S12 system, so as to accurately reconstruct S12 system from reduced lead system for diagnosis. In this paper, we propose a personalized reduced 3-lead (R3L) system formation methodology which employs principal component analysis, thereby, reducing redundancy and increasing SNR ratio, hence, making it suitable for wireless transmission. Accurate S12 system is made available using personalized lead reconstruction methodology, thus addressing medical constraints. Mean R2 statistics values obtained for reconstruction of S12 system from the proposed R3L system using PhysioNet's PTB and TWA databases were 95.63% and 96.37% respectively. To substantiate the superior diagnostic quality of reconstructed leads, root mean square error (RMSE) metrics obtained upon comparing the ECG features extracted from the original and reconstructed leads, using our recently proposed Time Domain Morphology and Gradient (TDMG) algorithm, have been analyzed and discussed. The proposed system does not require any extra electrode or modification in placement positions and hence, can readily find application in computerized ECG machines

Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks

The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data.Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled. This study proposes a method for generating time-series data based on GANs and explores their ability to generate biosignals with certain classes and characteristics. Moreover, in the proposed method, latent variables are analyzed using canonical correlation analysis (CCA) to represent the relationship between input and generated data as canonical loadings. Using these loadings, we can control the characteristics of the data generated by the proposed method. The influence of class labels on generated data is analyzed by feeding the data interpolated between two class labels into the generator of the proposed GANs. The CCA of the latent variables is shown to be an effective method of controlling the generated data characteristics. We are able to model the distribution of the time-series data without requiring domain-dependent knowledge using the proposed method. Furthermore, it is possible to control the characteristics of these data by analyzing the model trained using the proposed method. To the best of our knowledge, this work is the first to generate biosignals using GANs while controlling the characteristics of the generated data