e-bra With Nanosensors for Real Time Cardiac Health Monitoring and Smartphone Communication

Abstract

Mortality due to cardiac related ailments has been consistently higher in women as compared with men since the early 1980s in the United States. Gender related differences in specificity of regular noninvasive diagnostic tools and the lack of a clear understanding of the effect of postmenopausal hormonal changes in women have been cited as the two main reasons for this disparity. Recent advances in secondary and tertiary diagnostic information extraction techniques from signals such as electrocardiogram (ECG) through heart rate variability (HRV) analysis and wavelet domain analysis techniques have revealed many differences in autonomic nervous-cardiovascular activity regulation, between men and women. Moreover, the diagnostic tests for cardiovascular diseases usually start upon the manifestation of chest pain or angina. At this stage, disease management is the only option as opposed to preventive treatments, which is also possible with early detection based on the diagnostic information extraction techniques as stated previously. In order to truly realize the potential of such techniques, continuous and long-term monitoring is an essential requirement. This, in turn, requires sensor systems to be seamlessly mounted on day to day clothing for women. This paper describes an e-bra platform for nanosensors toward cardiovascular monitoring. The sensors, nanomaterial, or textile based dry electrodes acquire electrocardiograph, which is sent to a textile mounted wireless module. A smartphone or a wireless Bluetooth enabled PC can receive these data and store or process the information as necessary. In this paper, we wirelessly acquire ECG from subjects with the e-bra and perform HRV analysis on a PC. The use of a Smartphone as a base station for receiving data offers the advantage of cellular network connectivity to internet and consequently cloud computing resources for more complex computations such as feature extraction and automatic diagnosis. To address this capability, we further propose a protocol for response to emergencies from both the cloud backend and the smartphone itself.