Hypertension, a silent killer, is the biggest challenge of the 21 century in public health agencies worldwide [1]. World Health Organization (WHO) statistic shows that the mortality rate of hypertension is 9.4 million per year and causes 55.3% of total deaths in cardiovascular (CV) patients [2]. Early detection and prevention of hypertension can significantly reduce the CV mortality. We are presenting a wireless chest wearable vital sign monitoring platform. It measures Electrocardiogram (ECG), Photoplethsmogram (PPG) and Ballistocardiogram (BCG) signals and sends data over Bluetooth low energy (BLE) to mobile phone-acts as a gateway. A custom android application relays the data to thingspeak server where MATLAB based offline analysis estimates the blood pressure. A server reacts on the health of subject to friends & family on the social media - twitter. The chest provides a natural position for the sensor to capture legitimate signals for hypertension condition. We have done a clinical technical evaluation of prototypes on 11 normotensive subjects, 9 males 2 females.
Premature ventricular contraction (PVC) is the type of ectopic heartbeat, commonly found in the healthy population and is often considered benign. However, they are reported to adversely affect the accuracy of R-R variability based electrocardiographic (ECG) algorithms. This study proposes a Principal Component Analysis (PCA) based algorithmic approach to detect the PVCs based on their morphology. The eigenvectors were derived from signal window around the R-peak, where signal window for the PVC (wPVC) and that of NSR (wNSR) were set to 0.55 seconds and 0.16 seconds respectively. We used 24 ECG recordings from MIT BIH arrhythmia database as training dataset and the remaining 24 ECG recordings as testing dataset. Using the derived eigenvectors and the Linear regression (LR) analysis; complexes corresponding to the wNSR and wPVC were estimated from training and testing datasets. Four different classification methods were employed to differentiate between wPVS and wNSR, namely, Root mean squared error (RMSE), Pearson product-moment correlation coefficient comparision, Histogram probability distribution and k-Nearest Neighbour (KNN). All four methods were implemented individually to classify the wPVC and wNSR. The performance of each of the classification approach was evaluated by computing sensitivity and specificity. With the sensitivity of 93.45% and specificity of 93.14%, KNN based classification method has shown the best performance. The method proposed in this study allows for an effective differentiation between NSR beats and PVC beats.
Hypertension is a silent killer and one-third of its sufferers are unaware of its presence. Tonometric devices, like SphygmoCor, Compilor etc., represent the gold standard in pulse wave velocity (PWV) and augmentation index (AIx) measurements which are limited by their high cost and operational accuracy. Here, we present an alternative technology that is low cost and may be suitable for the 'wearable' setting. We undertook the comparisons of arterial waveforms obtained by photoplethysmogram (PPG) and finger ballistocardiogram (BPP) sensors which were then validated against a SphygmoCor tonometric device. Specifically, the agreement analysis of the augmentation, stiffness, reflection, elasticity, ejection elasticity and dicrotic reflection indexes showed that arterial distension waveform sensing using BPP sensor, has precision and accuracy similar to that of a SphygmoCor tonometric device whilst outperforming the volumetric arterial flow sensing using a PPG sensor, in every index. BPP indexes showed the r 2 fit of up to 0.95 and Spearman's rank correlation up to 0.91 when validated against the SphygmoCor tonometer. The estimated individual transfer functions for the BPP sensor, with reference to SphygmoCor, have accuracies of above 85% and 98% for 2 and 4-element windkessel (WK) models, respectively. The findings reported in this work may also be useful for the development of systems that are beneficial in the early and/or routine detection of hypertension.INDEX TERMS Augmentation index (AIx), finger ballistocardiogram (BPP), cuff-less blood pressure, electrocardiogram (ECG), photoplethysmogram (PPG), pulse wave velocity (PWV), stiffness index (SI), reflection index (RI).
Received signal strength (RSS) based localization of a source is a simple but effective technique. In RSS based localization source location is estimated by converting obtained signal into distance. In this paper, centroid and weighted centroid algorithms has been utilized to locate a partial discharge (PD) source. An artificial PD signal was generated, and signals were captured using radio frequency (RF) sensors and hence the location of the source was estimated. The location of the source was estimated for three different positions. There were eight measurement sensors used and received signal was converted into dBm as input to the location algorithm.
Heart rate variability analysis (HRVA) gives valuable insight to the cardiovascular system. Electrocardiogram (ECG) based HRVA has been assessment gold standard but eavesdropping of wearable technology requires the comparison of its surrogacy to an accepted standard. In this study, optical and mechanical measures at distal artery waveform are compared to the electrical signal of the heart. The sensor data of the six healthy volunteers are collated and compared at fiducial points in various time, frequency and non-linear domains for HRVA. We have found that during early systole fiducial location on waveforms can be surrogate to ECG standard and mechanical sensor 2 nd derivative proved to be the best among them. Also, the comparative technology shows enormous potential for cardiovascular diagnostic.
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