Voicu Groza

Sensor-based information appliances

Using the existing home infrastructure based on open industry standards, we will be able to integrate the home network with external networks to easily manage home devices, both locally and remotely. The advent of pervasive computing marks an urgent need for a new generation of intelligent sensing agents and information appliances. It will also demand environments for resource management of broad applications involving loosely coupled, event-driven, diverse information appliances. It is suggested that the development of intelligent sensing agents and sensor-based information appliances will spread pervasive technology to a multitude of human activities such as mining and manufacturing, security, transportation, sports, and health care. The topics discussed also include management of heterogeneous functions and networking technologies.

Electrocardiogram-Assisted Blood Pressure Estimation

Accurate automatic noninvasive assessment of blood pressure (BP) presents a challenge due to conditions like arrhythmias, obesity, and postural changes that tend to obfuscate arterial amplitude pulsations sensed by the cuff. Researchers tried to overcome this challenge by analyzing oscillometric pulses with the aid of a higher fidelity signal-the electrocardiogram (ECG). Moreover, pulse transit time (PTT) was employed to provide an additional method for BP estimation. However, these methods were not fully developed, suitably integrated, or tested. To address these issues, we present a novel method whereby ECG-assisted oscillometric and PTT (measured between ECG R-peaks and maximum slope of arterial pulse peaks) analyses are seamlessly integrated into the oscillometric BP measurement paradigm. The method bolsters oscillometric analysis (amplitude modulation) with more reliable ECG R-peaks provides a complementary measure with PTT analysis (temporal modulation) and fuses this information for robust BP estimation. We have integrated this technology into a prototype that comprises a BP cuff with an embedded conductive fabric ECG electrode, associated hardware, and algorithms. A pilot study has been undertaken on ten healthy subjects (150 recordings) to validate the performance of our prototype against United States Food and Drug Administration approved Omron oscillometric monitor (HEM-790IT). Our prototype achieves mean absolute difference of less than 5 mmHg and grade A as per the British Hypertension Society protocol for estimating BP, with the reference Omron monitor.

Coefficient-Free Blood Pressure Estimation Based on Pulse Transit Time–Cuff Pressure Dependence

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT-CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT-CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT-CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations.

Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load.

Measurement of Heart Rate Variability Using an Oscillometric Blood Pressure Monitor

We apply the maximal overlap discrete wavelet transform (MODWT)-based spectral density estimation method to measure heart rate variability (HRV) from short-duration pulse wave signals produced by an automated oscillometric blood pressure (BP) monitor during routine measurements. To test the accuracy of this wavelet HRV metric, we study the linear correlations that it achieves with chronological age and BP in a healthy population of 85 subjects. We define accuracy as the quality of the linear regression of HRV with age and BP. Results are compared with a number of traditional HRV metrics and earlier published work. The MODWT HRV metric achieves higher (and more significant) correlations with age and BP compared to other metrics. Moreover, these correlations are in agreement with earlier published work on correlations of HRV (measured from much longer duration electrocardiogram signals) with age and BP. As a further enhancement, we combine the MODWT HRV metric with other HRV metrics inside a multiple-linear-regression model and show an improvement in the correlations between the predicted and actual ages and the predicted and actual BP. Our work thus indicates the suitability of the MODWT metric either as a stand alone or in combination with other metrics for characterizing HRV from short-duration oscillometric pulse wave signals. Based on our results, we conclude that oscillometric BP monitors can be used to measure HRV in addition to measuring BP.

Assessment of algorithms for oscillometric blood pressure measurement

Three different algorithms for finding blood pressure through the oscillometric method were researched and assessed. It is shown that these algorithms are based on two different underlying approaches. The estimated values of systolic and diastolic blood pressure are compared against the nurse readings. The best two approaches turned out to be the linear approximation algorithm and the points of rapidly increasing/decreasing slope algorithm. Future work on combining these two algorithms using algorithm fusion is envisaged.

Intelligent robotic sensor agents for environment monitoring

This paper discusses development aspects of a wireless network of mobile autonomous Robotic Intelligent Sensor Agents deployed in the field for active investigation of multiple environmental parameters. The collected sensor data are fused in a world model, which is available to remote human supervisors as an interactive virtual Virtualized Reality/spl trade/ environment model.

Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is proposed based on pseudomeasurements using bootstrap principles to first derive the pseudomaximum amplitudes and then the pseudoenvelopes (PEs). The SBP and DBP are estimated using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. In order to reduce the amount of processing, a single-step methodology that directly derives PE using bootstrap principles is also presented. Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Students t-distribution method.

Oscillometric Blood Pressure Estimation: Past, Present, and Future

The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.

High-resolution floating-point ADC

Floating-point analog-to-digital converters (FP-ADC) are used for acquiring signals within a high dynamic range. The quantization range of FP-ADCs is several orders of magnitude higher than that of a fixed-point analog-to-digital converter (ADC). Comparing floating-point quantization with uniform quantization, it is shown that the FP-ADC is superior from a statistical point of view over the ADC, but at the price of doubling the conversion time. An improved FP-ADC that achieves a higher acquisition rate, while preserving the highest possible resolution, is presented. Simulation results illustrate the performance of the new FP-ADC.