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A novel pulse morphology-based approach for estimation of blood pressure from non-invasive oscillometric blood pressure measurement is presented. Quantitative measures that describe the pulse morphology are utilized to obtain the estimates of mean arterial, systolic, and diastolic pressures. Preliminary results obtained from a small set of measurem...
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... In our study, pulse width was significantly lower in the pregnant groups. Pulse width has been related to arterial stiffness with the formula stiffness index = subject height/change in time (or pulse width) [13]. Interestingly, our data also show no difference in pulse width or OMW amplitude between the HDP and HP groups. ...
Objective
Understanding of how oscillometric waveforms (OMW) vary between pregnant and nonpregnant individuals remains low. An exploratory analysis was completed to assess for quantitative and qualitative changes in OMW and oscillometric envelope features in pregnancy.
Design and methods
Eighteen pregnant individuals (over 20 weeks gestational age) and healthy, nonpregnant (HNP) women were recruited. Six HNP were matched to six healthy pregnant (HP) women, and six pregnant women with a hypertensive disorder of pregnancy (HDP) by age, arm circumference, and cuff size. Blood pressure measurements were completed per the International Organization for Standardization (ISO) protocol using a custom-built oscillometric device as the test device and two-observer mercury auscultation as the reference measurement. Auscultatory blood pressure and blood pressure derived from slope-based and fixed ratio algorithms were determined. OMW and envelope features were compared among groups.
Results
In HNP, HP, and HDP groups respectively: mean auscultatory blood pressure (systolic mean ± SD/diastolic mean ± SD) was 103.4 ± 12.2/67.1 ± 7.9; 109.5 ± 3.1/58.1 ± 6.4; 135.6 ± 18.9/85.1 ± 14.2 mmHg. HDP had significantly higher auscultatory systolic and diastolic blood pressure than the HP group ( P = 0.001). The pregnant groups had a lower average pulse width (mean ± SD: HNP = 0.8 ± 0 s, HP = 0.6 ± 0.1 s, HDP = 0.6 ± 0.1 s; HP vs. HNP mean difference [adjusted P value]: 0.2 [ P = 0.004], HDP vs. HNP 0.1 [ P = 0.018]) compared with the HNP group. The HDP group had a larger area under the OMW envelope than the HNP group (mean ± SD: HNP = 22.6 ± 3.4; HDP = 28.5 ± 4.2; HDP vs. HNP mean difference [adjusted P value]: 5.9 P = 0.05).
Conclusion
In this exploratory work, differences in the OMW morphology and parameters were found in pregnancy and in hypertensive disorders of pregnancy compared with healthy controls. Even small differences may have important implications in algorithm development; further work comparing OMW envelopes in pregnancy is needed to optimize the algorithms used to determine blood pressure in pregnancy.
... When it senses abnormality in blood pressure, it sends a signal to central nervous system as bar receptor reflexes. These receptors read the stretching of the arterial walls, heart and veins [16]. ...
Recent days many researches are carried out related to the heart of the human. As a result many remedies are found for the various heart disorders. Electrocardiography (ECG) is the exertion of the electrical response from heart by placing the electrodes over the chest. The Einthoven triangle is an imaginary formation of the three leads in the triangle used in electrocardiography; by that technique of placement of electrode we can analysis the electrocardiogram of the heart. The waveform is also known as PQR waveform which contains the information of arterial repolarization, arterial polarization, ventricular repolarization and ventricular polarization. In general the BP monitor is carried out using oscillometry. In the existing system separate techniques and devices are used to measure electrocardiography and BP. The proposed system is to integrate both electrocardiography and blood pressure measurement by means adopting transit pulse time. It is defined as the systematic time lay off betwixt oscillometric pulses and peaks of ECG especially R peaks of it. The major advantage of the system is to exhibit non-zero crossing and gives more accurate result. The mean arterial pressure estimation is done by using MatLab which is the unique estimation method for measuring pulse transit time interval. Thus the non invasive system designed which reduce the cuff deviations and errors in electrocardiography. It can be applied in detecting the arterial disorders at most efficient manner.
... A comprehensive survey of existing oscillometric BP estimation algorithms [13] reviews conventional MAA and derivative oscillometric algorithms [17], as well as those using a Bayesian approach [18], neural networks (NNs) [19], and fuzzy logic approaches and those analyzing oscillometric pulse morphology [16], [20]. These latter model the underlying, physics, anatomy, and physiology of the measurement system (cuff + arm characteristics) and use model parameters and regression methods to predict the systolic and diastolic pressures, which would produce the observed pressure oscillations [16]. ...
This paper presents a novel method of estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time domain features extracted from auscultatory and oscillometric waveforms and using Gaussian Mixture Models and Hidden Markov Model (GMM-HMM). The nine time domain features selected include the cuff pressure (CP), the cardiac period (T), the energy of the Korotkoff pulses (KE), the oscillometric waveform envelope (OWE), the lag between the trough of the oscillometric waveforms (OWs) and the peak of the Korotkoff energy (Lag), the time between the trough and the peak of the OW (OWD), the slopes of the KE and OWE (SKE, SOWE) and the maximum up-slope of individual OWs (MSOW). Adopting a 5-fold cross-validation scheme and using a data base of 350 non-invasive blood pressure (NIBP) recordings gave an average mean error (± standard deviation of error) of -0.3±4.2 mmHg for SBP and 2.9±8.1 mmHg for DBP relative to reference values derived from a visual method of determining systolic and diastolic blood pressure. The significantly larger spread of DBP estimates relative to SBP, suggests that the criteria for determining DBP are poorly defined and would benefit from further experimental studies involving simultaneous invasive and non-invasive methods of measuring arterial pressure. We conclude that the proposed GMM-HMM BP estimation method outperforms previously reported methods in the literature and is a very promising method improving the accuracy of automated non-invasive measurement of blood pressure.
... A comprehensive survey of existing oscillometric BP estimation algorithms [5] reviews conventional maximum amplitude algorithm (MAA) and derivative oscillometric algorithms [6], as well as those using a Bayesian approach [7], Neural Networks (NN) [8] and fuzzy logic approaches and those analysing oscillometric pulse morphology [9], [10]. Multiple linear regression and support vector regression have also been applied on a number of time-domain features derived from the OWE [11]. ...
In general, existing machine learning based approaches, developed for systolic and diastolic blood pressure (SBP and DBP) estimation from oscillometric waveforms (OWs), employ features extracted from the OW envelope (OWE) alone and ignore important beat-by-beat (BBB) features which represent fundamental physical properties of the entire non-invasive blood pressure (NIBP) measurement system. Unlike the existing literature, this paper proposes a novel deep-learning based method for BP estimation trained with BBB time-domain features extracted from OWs. First, we extract six time-domain features from each beat of the OW, relative to the preceding beat. Second, using the extracted BBB features along with the corresponding cuff pressures, we form a feature vector for each OW beat and locate it in one of three different classes, namely pre-systolic (PS), between systolic and diastolic (BSD) and after diastolic (AD). We then devise a deep-belief network (DBN)-deep neural network (DNN) classification model as well as a novel artificial feature extraction method for estimating SBP and DBP from feature vectors extracted from OWs and their corresponding deflation curves. The proposed DBN-DNN classification approach can effectively learn the complex nonlinear relationship between the artificial feature vectors and target classes. The SBP and DBP points are then obtained by mapping the beats at which the network output sequence switches from PS phase to BSD phase and from BSD phase to AD phase, respectively, to the deflation curve. Adopting a 5-fold cross-validation scheme and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.1±2.9 mmHg for SBP and 3.0±5.6 mmHg for DBP relative to reference values. We experimentally show that the proposed DBN-DNN-based classification algorithm trained with BBB timedomain features can outperform traditional deep-learning based methods for BP estimation trained with features extracted only from OWEs.
... We reported in the introduction that the MAA is most commonly used in oscillometric NIBP monitors, although many other BP estimation algorithms have been proposed [20][21][22][23][26][27][28][29]. [16] as between 0.45 and 0.73 and 0.69 to 0.83, respectively. ...
We present a robust method for testing and calibrating the performance of oscillometric non-invasive blood pressure (NIBP) monitors, using an industry standard NIBP simulator to determine the characteristic ratios used, and to explore differences between different devices. Assuming that classical auscultatory sphygmomanometry provides the best approximation to intra-arterial pressure, the results obtained from oscillometric measurements for a range of characteristic ratios are compared against a modified auscultatory method to determine an optimum characteristic ratio, Rs for systolic blood pressure (SBP), which was found to be 0.565. We demonstrate that whilst three Chinese manufactured NIBP monitors we tested used the conventional maximum amplitude algorithm (MAA) with characteristic ratios Rs = 0.4624±0.0303 (Mean±SD) and Rd = 0.6275±0.0222, another three devices manufactured in Germany and Japan either do not implement this standard protocol or used different characteristic ratios. Using a reference database of 304 records from 102 patients, containing both the Korotkoff sounds and the oscillometric waveforms, we showed that none of the devices tested used the optimal value of 0.565 for the characteristic ratio Rs, and as a result, three of the devices tested would underestimate systolic pressure by an average of 4.8mmHg, and three would overestimate the systolic pressure by an average of 6.2 mmHg.
... The oscillometric waveform itself conveys hemodynamic information [18], whereas the OMWE is prone to artifacts and multiple influences [14][15][16]19]. Mafi et al. [20,21] looked at the PP waveform modulation and the maximum upslope of the systolic peak to improve NIBP. Some authors investigated the advantages of simultaneously recording ECG. ...
... Our SFATI technique does not rely on the OMWE and MBP determination but on the time-domain analysis of the pulse waveform for immediate SBP assessment. It appears intrinsically different of all previously published approaches, although it is in line with the works of Forouzanfar et al. [17] or Mafi et al. [20], and may be related to PAT and pulse transit time studies [22]. ...
Background:
Noninvasive blood pressure (BP) measurement is essential for the study of human physiology but automatic oscillometric devices only estimate SBP and DBP using various, undisclosed algorithms, precluding standardization and interchangeability. We propose a novel approach by tracking, during pneumatic cuff deflation, the time interval from the foot to the apex of the systolic peak of the oscillometric signal, which reaches a maximum concomitant with the first Korotkoff sound.
Method:
In 145 study participants and patients (group 1), we measured the systolic brachial artery blood pressure by Korotkoff sound recording, conventional oscillometry, and our fully automated systolic peak foot-to-apex time interval (SFATI) technique. In 35 other patients (group 2), we compared SFATI with intra-arterial measurement.
Results:
In group 1, the concordance correlation coefficient was 0.989 and 0.984 between SFATI and Korotkoff sounds, 0.884 and 0.917 between oscillometry and Korotkoff sounds, and 0.882 and 0.919 between SFATI and oscillometry, respectively, on the left and right arm. In group 2, it was 0.72 between SFATI and intra-arterial measurement, 0.67 between oscillometry and intra-arterial measurement, and 0.92 between SFATI and Korotkoff sounds. In 40 study participants, the reproducibility study yielded a concordance coefficient of 0.95 for SFATI and 0.94 for Korotkoff sounds.
Conclusion:
SFATI BP measurement shows an excellent concordance with the auscultatory technique, offering a major improvement over current oscillometric techniques and allowing standardization.
... Many researchers are working towards developing a reliable, affordable, accurate and easy to use BP monitor. Mafi et al. [3] developed a cuff-based hardware that records the oscillometric wave and proposed a pulse morphology based approach for estimating BP. Samria et al. [4] designed a finger PPG sensor using an infra-red LED and Ahmed et al. [5] developed a prototype that records PPG from the user's head region (temple). ...
This paper presents a cuff-less hypertension pre-screening device that non-invasively monitors the Blood Pressure (BP) and Heart Rate (HR) continuously. The proposed device simultaneously records two clinically significant and highly correlated biomedical signals, viz., Electrocardiogram (ECG) and Photoplethysmogram (PPG). The device provides a common data acquisition platform that can interface with PC/laptop, Smart phone/tablet and Raspberry-pi etc. The hardware stores and processes the recorded ECG and PPG in order to extract the real-time BP and HR using kernel regression approach. The BP and HR estimation error is measured in terms of normalized mean square error, Error Standard Deviation (ESD) and Mean Absolute Error (MAE), with respect to a clinically proven digital BP monitor (OMRON HBP1300). The computed error falls under the maximum standard allowable error mentioned by Association for the Advancement of Medical Instrumentation; MAE <; 5 mmHg and ESD <; 8mmHg. The results are validated using two-tailed dependent sample t-test also. The proposed device is a portable low-cost home and clinic bases solution for continuous health monitoring.
... Forouzanfar et al ont passé en revue les principales solutions techniques proposées (72) . Certains auteurs proposent de se référer, pour déterminer ces valeurs, non pas aux inflexions de la dérivée de l'enveloppe de la courbe oscillométrique, mais à celle de chaque pulsation, et à la morphologie-même des oscillations (analyse dans le domaine temporel) (73) . D'autres encore proposent la variance pondérée du signal oscillométrique, qui prédomine entre la pression systolique et la pression diastolique (49) , ou de procéder à une décomposition modale empirique (74) . ...
... L'observation des changements de forme et de délai d'arrivée de l'onde artérielle (ou onde de pouls) au cours du dégonflage du brassard pneumatique apporte des informations importantes. Les méthodes d'analyse morphologique de l'onde de pouls exploitent cette évolution et se fondent sur la modulation de l'onde pour déterminer les pressions systolique, diastolique et moyenne (79,73) . Les méthodes de calcul du temps de transit de l'onde artérielle sont basées sur la mesure du délai entre l'onde R du signal ECG et l'arrivée de l'onde, lors du même cycle cardiaque, en aval du brassard pneumatique lorsque sa pression devient inférieure à la pression systolique (80) . ...
... The changes in shape and delay of the arterial pulse during cuff deflation appear to provide relevant information. Arterial pulse morphology methods empirically exploit changes in pulse amplitude, duration, and modulation to identify SBP, MAP, and DBP (79,73) . Pulse transit-time (PTT) methods require measurements of the delay between the R wave on the ECG and the onset of the arterial pulse (80) , which can be detected downstream from the cuff as soon as the cuff pressure becomes lower than SBP. ...
Notre travail de thèse est consacré au développement d’une nouvelle approche d’analyse du signal oscillométrique pour mesurer la pression artérielle systolique et identifier les personnes dont la paroi artérielle est anormalement rigide. L’oscillométrie, largement exploitée pour la mesure automatique non-vulnérante de la pression artérielle, repose sur l’amplitude des variations dynamiques de pression du brassard pneumatique générées par l’expansion de l’artère brachiale sous l’effet de l’onde de pouls. Nous avons d’abord effectué une revue de la littérature sur les méthodes auscultatoire et oscillométrique. La méthode auscultatoire, fondée sur la détection des bruits produits par l’artère brachiale sous le brassard, reste la référence pour la validation des moniteurs oscillométriques. Depuis la description de ces bruits par Nicolaï Korotkoff en 1905, de nombreux auteurs ont tenté d’en expliquer l’origine et d’en identifier les limites et pièges en comparaison avec la mesure intra-artérielle directe. La technique oscillométrique dérive de l’invention du sphygmographe par Etienne-Jules Marey en 1859. Les constructeurs procèdent à la validation de leurs appareils en référence aux normes internationales (ISO) sans dévoiler les algorithmes mise en œuvre. De très nombreuses approches ont été proposées, depuis des rapports déterminés empiriquement jusqu’à des réseaux de neurones en passant par divers modèles mathématiques, pour déterminer les pressions systolique et diastolique à partir de la pression moyenne mesurée sur la courbe oscillométrique. Cependant, l’oscillométrie donne des résultats variables et présente des erreurs significatives, en particulier pour la détermination de la pression systolique, notamment chez les sujets ayant des facteurs de risque cardiovasculaires.Sur la base de cette analyse, considérant que la référence reste la détection des bruits de Korotkoff, nous avons cherché à en mieux comprendre les mécanismes. Nous avons enregistré les images échographiques et le signal Doppler de l’artère brachiale sous le brassard lors de la mesure de pression artérielle chez des sujets volontaires, en synchronisation avec l’ECG, la pression du brassard et les bruits de Korotkoff. Nous avons pu observer les variations cycliques du diamètre de l’artère brachiale pendant le dégonflage du brassard, et mesurer la vitesse locale de propagation de l’onde de pouls, ainsi que les délais entre le signal oscillométrique, l’ECG et les bruits de Korotkoff. Nous avons pu ainsi démontrer que les bruits de Korotkoff sont produits par la vibration de la paroi artérielle sous l’impact de l’onde de pouls, puis par la turbulence de l’écoulement flux sanguin, et nous avons montré la diminution marquée de la vitesse locale de l’onde de pouls lorsque la pression du brassard réduit la pression artérielle transmurale. L’observation de ces enregistrements nous a montré l’intérêt de l’analyse de la forme de l’onde de pouls enregistrée par oscillométrie. Nous en avons tiré une approche innovante fondée sur l’analyse temporelle pour la détermination directe de la pression artérielle systolique. Nous avons réalisé une étude clinique prospective, selon un protocole approuvé par le Comité d’éthique du CHU de Nîmes, pour valider notre nouvelle approche. Nous avons comparé notre technique à la méthode auscultatoire chez 145 sujets avec ou sans facteurs de risque cardiovasculaire, et à la pression mesurée par cathéter radial chez 35 patients hospitalisés en réanimation. Nous avons obtenu une excellente concordance avec le premier bruit de Korotkoff, avec des résultats très supérieurs à l’oscillométrie réalisée à l’aide d’un appareil validé. De plus, notre technique s’est montrée capable d’identifier les sujets porteurs de facteurs de risque cardiovasculaires, se comparant favorablement à la vitesse de l’onde de pouls aortique.
... As expressed earlier, various attempts have been made to characterize pulses and determine blood pressure using quantitative and mathematical modeling measures [20] [48] [49]. ...
... Testing and analysis using statistical tools was done by comparing the proposed method to reference algorithms, pulse morphology [20] and the OMRON HEWM-790 ITCAN device. ...
... Pulses can be observed while recording an individual's blood pressure. Thus, a pulse waveform can be defined as a combination of blood pressure pulses over a measurement cycle [27] [31].Parameters describing oscillometric pulse wave are discussed in literature[20] [37 -39] [41]. ...
... In [18] and [19], different techniques of detecting the maximum-amplitude oscillometric pulse were compared and it was found that having goodquality oscillometric pulses is important for improving the MAP estimation results. As an alternative, a few oscillometric pulse morphology-based methods have been proposed for estimation of MAP [20], [21]. These methods are based on extracting various features of the individual pulses and studying their changes as a function of CP. ...
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.
