Wireless Chest Wearable Vital Sign Monitoring Platform for Hypertension

Hypertension, a silent killer, is the biggest challenge of the 21 st century in public health agencies worldwide. 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. 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 and 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

Heartrate Variability Comparison Between Electrocardiogram, Photoplethysmogram and Ballistic Pulse Waveforms at Fiducial Points

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 2nd derivative proved to be the best among them. Also, the comparative technology shows enormous potential for cardiovascular diagnostic

Novel non-invasive Pressure-Volume Loop measurement for local Pulse Wave Velocity estimation

In the last four decades, hypertension doubled to 1.13 billion patients. High blood pressure (BP) is the main risk factor for cardiovascular morbidity and mortality. Arterial stiffness (AS) is a key component and poorly understood part of cardiac vital signs. Pressure-volume loop (PU-Loop) has been used to measure local pulse wave velocity (PWV) which is an indicator of AS [1]. We have been able to measure the PU-Loop non-invasively on palmar digital arteries. Pressure and flow waveforms are measured simultaneously at the same location. The dataset has calculated the normalized PWV of 1.48±0.4 from the slope of the line formed between two early systole points of 20-30%. PU-Loop provides an insight into contractility, preload, and hypertension and correcting factor for pulse transit time estimations

Evaluation of pulse transit time for different sensing methodologies of arterial waveforms.

We perform a novel comparative analysis between optically and mechanically derived pulse transit time (PTT), which is a universally employed technique for cuffless blood pressure (BP) estimation. Two inline photoplethysmogram (PPG) sensors were placed at the distal and proximal phalanxes of the index finger, and two finger ballistocardiogram (BPP) sensors were wrapped on top of the PPG sensors fixture around the phalanxes of the index finger. The stacking of the BPP sensor over the PPG sensor provided vertical spatial alignment for same location acquisition of the blood flow waveform through the radial artery. The analysis of variance (ANOVA) between PTT derived from the PPG and BPP sensors resulted in a statistically significant difference at p < 0.05. The PTT derived from the BPP sensors showed higher values (17.8 milliseconds on average) than the PTT derived from the PPG sensors. Higher accuracy PTT values will improve the estimation of cuffless BP and thus has the potential to revolutionize the technology

The effects of 40 Hz low-pass filtering on the spatial QRS-T angle

The spatial QRS-T angle (SA) is a vectorcardiographic (VCG) parameter that has been identified as a marker for changes in the ventricular depolarization and repolarization sequence. The SA is defined as the angle subtended by the mean QRS-vector and the mean T- vector of the VCG. The SA is typically obtained from VCG data that is derived from the resting 12-lead electrocardiogram (ECG). Resting 12-lead ECG data is commonly recorded using a low-pass filter with a cutoff frequency of 150 Hz. The ability of the SA to quantify changes in the ventricular depolarization and repolarization sequence make the SA potentially attractive in a number of different 12-lead ECG monitoring applications. However, the 12-lead ECG data that is obtained in such monitoring applications is typically recorded using a low-pass filter cutoff frequency of 40 Hz. The aim of this research was to quantify the differences between the SA computed using 40 Hz low- pass filtered ECG data (SA40) and the SA computed using 150 Hz low-pass filtered ECG data (SA150). We assessed the difference between the SA40 and the SA150 using a study population of 726 subjects. The differences between the SA40 and the SA150 were quantified as systematic error (mean difference) and random error (span of Bland-Altman 95% limits of agreement). The systematic error between the SA40 and the SA150 was found to be -0.126° [95% confidence interval: -0.146° to - 0.107°]. The random error was quantified 1.045° [95% confidence interval: 0.917° to 1.189°]. The findings of this research suggest that it is possible to accurately determine the value of the SA when using 40 Hz low-pass filtered ECG data. This finding indicates that it is possible to record the SA in applications that require the utilization of 40 Hz low-pass ECG monitoring filters

The effects of electrode placement on an automated algorithm for the detection of ST segment changes on the 12-lead ECG

In this study we investigate the effect that ECG electrode placement can have on the detection of ST segment changes. BSPMs from 45 subjects undergoing PTCA were analysed (15 left anterior descending, 15 left circumflex and 15 right coronary artery). 12-lead ECG were extracted from BSPMs corresponding with correct precordial electrode positioning and corresponding with simultaneous vertical movement of all of the precordial leads in 5mm increments up to +/-50mm away from the correct position. A computer algorithm was developed based on current guidelines for the detection of STEMI and Non-STEMI. This algorithm was applied to all of the extracted 12-lead ECGs. Median sensitivity and specificity, based upon all baseline versus all peak balloon inflation cases, were calculated for results generated at each electrode position. With the precordial leads positioned correctly the sensitivity and specificity were 51.1% and 91.1% respectively. When all precordial leads were placed 50mm superior to their correct position the sensitivity increased to 57.8% whilst specificity remained unchanged. At 50mm inferior to the correct position the sensitivity and specificity were 46.7% and 88.9% respectively. The results show a variation of more than 10% in sensitivity when the electrodes are moved up to 100mm vertically

Automated detection of atrial fibrillation using RR intervals and multivariate-based classification

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%)