Saif Ahmad

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.

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.

A prototype of an integrated blood pressure and electrocardiogram device for multi-parameter physiologic monitoring

We present a prototype of an integrated blood pressure (BP) and electrocardiogram (ECG) device for multi-parameter physiologic monitoring. A standard BP pressure cuff and an ordinary wristband have been modified to incorporate in them dry ECG electrodes made of thin conductive fabric. The modified BP cuff and wristband are coupled with commercially available hardware and software to harvest simultaneous arterial pulse wave and ECG data from the arm and wrist of the other hand. Software has been written for assessing multiple physiologic parameters from the harvested pulse wave and ECG signals. We provide an initial validation of the performance of our prototype by conducting a study on six healthy subjects.

Wavelet estimation of pulse rate variability from oscillometric blood pressure measurements

We propose a wavelet-based spectral density estimation method for characterizing pulse rate variability of short duration oscillometric blood pressure signals produced by a digital blood pressure monitor during routine measurements. To validate our wavelet metric we compare its performance with other techniques by studying correlations of pulse rate variability with age and mean arterial pressure. Our results indicate that the proposed wavelet metric offers a superior and accurate characterization of variability of short duration oscillometric blood pressure signals.

Multiparameter Physiological Analysis in Obstructive Sleep Apnea Simulated With Mueller Maneuver

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.

Model-Based Mean Arterial Pressure Estimation Using Simultaneous Electrocardiogram and Oscillometric Blood Pressure Measurements

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.

Metrological Characterization of a Method for Blood Pressure Estimation Based on Arterial Lumen Area Model

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.

Characteristic Ratio-Independent Arterial Stiffness-Based Blood Pressure Estimation

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.

Dynamic threshold algorithm to evaluate trustworthiness of the estimated blood pressure in oscillometry

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.