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In this brief review, we will introduce arterial stiffness, describe how it is currently measured and point to new developments that might lead towards arterial stiffness estimation outside of specialized clinics in the future. In almost all methods for measuring arterial stiffness, the arterial pulse plays an important role. Therefore, new developments are related to novel non-invasive sensors for acquiring the arterial pulse, simulation and modeling of the arterial pulse waves, parameter estimation of the stiffness parameters through physical models of the arterial pulse and machine learning methods for predicting the arterial stiffness. [ABSTRACT FROM AUTHOR]

Noninvasive blood pressure (BP) measurement is an important tool for managing hypertension and cardiovascular disease. However, automated noninvasive BP measurement devices, which are usually based on the oscillometric method, do not always provide accurate estimation of BP. It has been found that change in arterial stiffness (AS) is an underlying mechanism of disagreement between an oscillometric BP monitor and a sphygmomanometer. This problem is addressed by incorporating parameters related to AS in the algorithm for BP measurement. Pulse transit time (PTT) is first used to estimate AS parameters, which are fixed into a model of the oscillometric envelope. This model can then be used to perform curve fitting to the measured signal using only four parameters: systolic BP, diastolic BP, mean BP, and lumen area at zero transmural pressure. The proposed technique is independent of the experimentally determined characteristic ratios that are commonly used in existing oscillometric methods. The accuracy of the proposed technique was evaluated by comparing with the same model without incorporation of AS, and with reference BP device measurements. The new method achieved standard deviation of error less than 8 mmHg and mean error less than 5 mmHg. The results show consistency with ANSI/AAMI SP-10 standard for noninvasive BP measurement techniques. [ABSTRACT FROM AUTHOR]

Accuracy of blood pressure (BP) measurement is a challenging issue in oscillometry. Most of the automated noninvasive BP monitors estimate BP from envelope of the measured oscillometric pulses. The peak and the trough of the oscillometric pulses are very sensitive to noise caused by breathing, heart-rate variability, motion artifacts, muscle contraction, and environmental noise. Therefore, accuracy of the estimated BP based on the oscillometric waveform envelopes is not reliable in some cases. Recently, employing a modeling approach to estimate BP, we obtained the accurate results for a set of healthy subjects. The method is based on the lumen area oscillations model and estimates BP by comparing the actual and corresponding simulated waveforms. The method’s accuracy worsened when we tested it on a broader range of healthy subjects, while a significant drop was observed when the method was used for patients with chronic cardiovascular diseases. The work presented in this paper represents an improved version of our previous approach. We tested the proposed method on both healthy subjects and patients with chronic cardiovascular diseases, and compared the results to two popular BP estimation algorithms: maximum amplitude algorithm and maximum/minimum slope algorithm. We observed up to 56.7% and 57.3% improvements in mean absolute error, 98.9% and 64.4% improvements in mean error, 50% and 59% improvements in standard deviation of errors, and up to 57.6% and 55.8% in measurement uncertainty for the estimated systolic and diastolic pressures, respectively. [ABSTRACT FROM AUTHOR]

Objectives: The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. Methods: A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Results: Our proposed pattern classification system achieved accuracies of up to $93$% in stationary subject detection, $90$% in stop-breathing detection, and $86$% in interference detection. Conclusion: Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Significance: Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) readings in oscillometry are very sensitive to the posture of the body, arm, and body movements during the BP measurements, so measuring conditions are the first important factors for trusted BP readings. Next is the BP estimation algorithm, which is responsible to convert the cuff deflation curve (CDC) pressure signal to accurate BP readings. With proper measuring conditions and an accurate BP estimation algorithm one can expect trusted BP readings. Trustworthiness of the BP readings is still a challenging issue in automated oscillometric BP monitors, and patients need to see the doctor for trusted measurements. To this end, we have proposed a novel method called a Dynamic Threshold Algorithm (DTA) that evaluates trustworthiness of the BP readings immediately after the BP is estimated, such that the patient can decide whether to repeat the measurement or not. DTA employs the heart rate (HR) of the subject and determines a specific threshold (TR). TR is used to determine maximum and minimum limits for trustable pressures (SBP2, DBP2) of a given subject. The limits are called trusted boundaries (TB). Trusted boundaries are compared with the estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) to determine trustworthiness of the measured BP. BP readings are trusted if estimated SBP and DBP are inside the TB and untrusted or labeled an outlier if otherwise. In this research, DTA is applied on three different data- sets of healthy and sick subjects, outliers are determined and removed from the datasets, and remaining recordings are validated against references and compared with validated results of original datasets. According to observations, improvements were significant after outliers were removed from the datasets. [ABSTRACT FROM PUBLISHER]

An accurate noninvasive estimation of mean arterial pressure (MAP) is of great importance in the evaluation of circulatory function and prognosis of some cardiovascular diseases. This paper proposes a novel oscillometric MAP estimation method based on the dependence of pulse transit time (PTT) on cuff pressure (CP). The PTT computed as the time interval between the electrocardiogram (ECG) R-peaks and the maximum slope points on the oscillometric pulses is mathematically modeled by considering the cuff-arm-artery system and the blood flow dynamics. It is then analytically shown that MAP can be approximated as the CP at which the PTT is maximum. Based on our theoretical findings, a new method of MAP estimation from simultaneous ECG and oscillometric blood pressure measurements is proposed. Our proposed method is validated with a pilot study in which 150 recordings from 10 subjects are analyzed. The reference MAP is computed from the systolic and diastolic pressures measured by the Food and Drug Administration-approved Omron HEM-790IT monitor using three different formulas given in the literature. The performance of our proposed method is compared with the maximum amplitude and zero-crossing methods in terms of mean error (ME), mean absolute error, and standard deviation of error (SDE). It is found that our proposed method achieves improvements of more than 20% in SDE compared with the maximum amplitude method and more than 50% in ME compared with the zero-crossing method. [ABSTRACT FROM PUBLISHER]

Sleep apnea is a common condition that poses serious health risks. Current state-of-the-art technology for diagnosing and assessing sleep apnea is obtrusive, expensive, and lacks integration/analysis of important cardiovascular and respiratory parameters. This paper evaluates a novel unobtrusive multiparameter sleep apnea monitor, namely, the Biopeak chest belt monitor, and describes a collection of associated algorithms for the detection of sleep apnea and assessment of the extent of the resulting physiological disturbance. The system uses dry electrodes to acquire electrocardiogram, respiratory, and stroke volume data and analyzes multiple physiological parameters from it. In a pilot investigation on eight healthy subjects simulating a total of 66 obstructive sleep apnea (OSA) events with Mueller maneuvers, the proposed system achieves an accuracy of 95.6%, a specificity of 97.2%, and a sensitivity of 93.8% for identifying these events. Moreover, the response of the simulated OSA events on the multiple physiological parameters conforms to earlier published work. We thus conclude that the proposed chest belt system has potential to be used for reliable sleep apnea detection and comprehensive evaluation of cardiovascular and respiratory parameters in sleep apnea. [ABSTRACT FROM PUBLISHER]

Breathing causes fluctuations in blood pressure and contributes to variations in blood pressure estimates. In order to reduce the variability in the blood pressure estimates induced by breathing, either the breathing signal should be removed from the oscillometric blood pressure signal, or its effects should be suppressed. This paper presents a hybrid method that combines homomorphic and adaptive signal processing techniques to extract the breathing signal from the oscillometric signal with or without a simultaneously recorded electrocardiogram (ECG). The quality of the extracted breathing signal and the depth of breathing are assessed using the reference breathing signals. The breathing signals extracted using the accompanying ECG signal were found to be superior in quality compared to the ones extracted from the oscillometric waveform. The blood pressure estimates were evaluated before and after the breathing suppression techniques were implemented. As a result of the breathing suppression, the fluctuation of the systolic and diastolic blood pressure estimates obtained from a database of 85 healthy subjects is reduced. [ABSTRACT FROM AUTHOR]

Oscillometry is the most common measurement method used in electronic blood pressure (BP) monitors. However, most of the existing oscillometric algorithms employ empirical ratios on the oscillometric waveform envelope (OMWE) to determine the systolic pressure (SP) and diastolic pressure (DP). As these algorithms do not consider the cardiovascular system parameters that may vary due to health conditions or age, the pressure estimates are not always reliable. In this paper, we develop a new mathematical model for the OMWE by incorporating an existing model of the cuff-arm-artery system. [ABSTRACT FROM PUBLISHER]

This paper introduces a novel approach to estimate the systolic and diastolic blood pressure ratios (SBPR and DBPR) based on the maximum amplitude algorithm (MAA) using a Gaussian mixture regression (GMR). The relevant features, which clearly discriminate the SBPR and DBPR according to the targeted groups, are selected in a feature vector. The selected feature vector is then represented by the Gaussian mixture model. The SBPR and DBPR are subsequently obtained with the help of the GMR and then mapped back to SBP and DBP values that are more accurate than those obtained with the conventional MAA method. [ABSTRACT FROM PUBLISHER]