Oscillometric Waveform Research Articles

Preventing troublesome variability in clinical blood pressure measurement.

The importance of accurate blood pressure (BP) measurement is well-established. However, there is little quantitative comparative data on the influence of different measurement conditions on BP or the stability of the oscillometric pulse waveform from which BP is calculated. This study investigates the effect of six different measurement conditions (Quiet, Reading, Speaking, Deep Breathing, Moving, and Tapping) on BP readings in 30 healthy normotensive subjects. We hypothesize that non-standard conditions will result in significant deviations in BP measurements compared to the Quiet condition. Additionally, the quality and stability of the oscillometric waveform were assessed by evaluating the smoothness of the oscillometric pulse waveform characteristics during cuff deflation. Compared with the Quiet condition, all others resulted in significantly higher blood pressures, except for the respiratory condition, which resulted in significantly lower BPs. Average subject systolic BP (SBP) rose from 117.4 ± 6.0 mmHg for the Quiet condition to 129.7 ± 7.2 mmHg for the light finger tapping condition, P < 0.00001. Overall, changes for diastolic BP (DBP) were not significant. For the quality Noise indicator, all conditions in comparison with the Quiet condition were significantly noisier, increasing from 0.144 ± 0.024 to 1.055 ± 0.308 mmHg, P < 0.00001. Changes in SBP were significantly correlated to changes in quality Noise (r = 0.965, P < 0.001). This study confirms the importance of following international BP measurement guidelines, providing quantitative data showing significant changes in BP when guidelines are not followed. A method for assessing the quality of the measurement is also demonstrated, and shows that BP changes are significantly related to changes in the quality indicator.

Oscillometric method: what is it and is it suitable for measuring blood pressure in all patients?

The oscillometric method, which is gradually replacing the traditional auscultatory method, is attractive to practitioner due to the possibility of automating blood pressure (BP) measurements. However, there is little literature information about its limitations. The purpose of this review is to address such issues. For this purpose, the main modern information on the maximum amplitude algorithm for BP measurement and location features, in which it is possible to obtain an oscillometric waveform, are preliminarily considered. In addition, the limitations and disadvantages of the method associated with both the equipment and the characteristics of various population groups are analyzed. The list of groups in which blood pressure measuring devices may be inaccurate has expanded significantly in the 2020s compared to the previous decade. This suggests that the reliability of the interpretation of data obtained by the oscillometric method in a certain part of patients is limited.

Evaluation of the oscillometric method for noninvasive blood pressure measurement during cuff deflation and cuff inflation with reference to intra-arterial blood pressure.

There is little quantitative clinical data available to support blood pressure measurement accuracy during cuff inflation. In this study of 35 male and 5 female lightly anaesthetized subjects aged 64.1 ± 9.6 years, we evaluate and compare the performance of both the oscillometric ratio and gradient methods during cuff deflation and cuff inflation with reference to intra-arterial measurements. We show that the oscillometric waveform envelopes (OWE), which are key to both methods, exhibit significant variability in both shape and smoothness leading to at least 15% error in the determination of mean pressure (MP). We confirm the observation from our previous studies that K1 Korotkoff sounds underestimate systolic blood pressure (SBP) and note that this underestimation is increased during cuff inflation. The estimation of diastolic blood pressure (DBP) is generally accurate for both the ratio and the gradient method, with the latter showing a significant increase during inflation. Since the gradient method estimates SBP and DBP from points of maximum gradient on each OWE recorded, it may offer significant benefits over the ratio method. However, we have shown that the ratio method can be optimized for any data set to achieve either a minimum mean error (ME) of close to 0 mmHg or minimum root mean square error (RMSE) with standard deviation (SD) of <5.0 mmHg. We conclude that whilst cuff inflation may offer some advantages, these are neither significant nor substantial, leaving as the only benefit, the potential for more rapid measurement and less patient discomfort.

A comparison among oscillometric waveforms in healthy nonpregnant women, pregnancy and hypertensive disorders of pregnancy.

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. 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. 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). 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.

Noninvasive Blood Pressure Monitoring via a Flexible and Wearable Piezoresistive Sensor.

In the present global context, continuous blood pressure (BP) monitoring is paramount in addressing the global mortality rates attributed to hypertension. Achieving precise insights into the human cardiovascular system necessitates accurate measurement of BP, and the accuracy depends on the faithful recording of oscillations or pulsations. This task ultimately depends on the caliber of the pressure sensor embedded in the BP device. In this context, we have fabricated a flexible resistive pressure sensor based on reduced graphene oxide (rGO) and a polydimethylsiloxane (PDMS) sponge that is highly flexible and sensitive. The designed device operates effectively with a minimal bias voltage of 500 mV, at which point it showed its maximum relative change in current, reaching approximately 25%. Additionally, the sensing device showed a notable change in resistance values, exhibiting almost 100% change in resistance when subjected to a pressure of 400 mmHg and high sensitivity of 0.27 mmHg-1. After promising outcomes were obtained during static pressure measurement, the sensor was used for BP monitoring in humans. The sensor accurately traced the oscillometric waveform (OMW) for distinct systolic blood pressure (SBP) and diastolic blood pressure (DBP) combinations to cover a range of medical situations, including hypotension, standard or normal, and hypertension. The values of SBP, DBP, and MAP were derived from the sensor's output using the MAA technique, and the errors in these values concerning the simulator and the traditional BP monitor follow the universal AAMI/ESH/ISO protocols. Bland-Altman (B&A) correlation and scatter plots were used to compare the sensor's results and further validate the proposed sensor. The sensor showed the mean and standard deviation error in the SBP, DBP, and MBP of -0.2 ± 5.9, -0.5 ± 7, and -0.9 ± 4.7 mmHg when compared with the noninvasive blood pressure (NIBP) simulator. The pulse rate (PR) was also calculated from the same OMW for the specified value of 80 beats per minute (bpm) given by the simulator and reported a mean PR value of ∼81 bpm, close to the reference value. The findings show that the flexible resistive sensing device can accurately measure BP and replace the existing sensors of BP devices.

Accuracy of oscillometric-based blood pressure monitoring devices: impact of pulse volume, arrhythmia, and respiratory artifact.

An oscillometric waveform (OMW) envelope-based blood pressure (BP) monitoring device is widely used to monitor blood pressure and prevent hypertension and adverse cardiovascular events. At present, all primary care physicians and clinicians widely recommend oscillometric-based BP devices. The consumer selects the device based on their own decision, without knowing whether the device is validated or not, resulting in over- or under-treatment of hypertension. It is imperative that each device must comply with international protocols. In this study, we have investigated the accuracy of inflation and deflation-based oscillometric BP monitoring devices in the case of sinus rhythm (SR). Since different health conditions of the patient affect the oscillometric waveform, which can affect the device's accuracy, in such cases, many BP monitors are skeptical of succeeding in the norms of international protocols. Therefore, this study also aims to calculate the accuracy of these devices in various health conditions and measure the effect of pulse volume, arrhythmia, and respiratory artifact on it using a non-invasive blood pressure (NIBP) simulator. We found that the oscillometric BP devices failed to measure the correct blood pressure in several clinical conditions.

OFFICE BLOOD PRESSURE VARIABILITY: WITHIN SESSION STABILITY ACROSS CONSECUTIVE MEASUREMENTS USING OSCILLOMETRIC DEVICES

Objective: Blood pressure (BP) is routinely monitored by healthcare providers and during drug development. For improvement in accuracy, several oscillometric devices perform multiple sequential inflations, rather than relying on a single inflation, to determine a mean systolic and diastolic blood pressure (SBP, DBP). Blood pressure is known to vary physiologically throughout a day, and over the course of days and weeks, due to intrinsic and extrinsic factors. Little data has been published describing the variability of SBP and DBP recorded minutes apart using oscillometric methods (i.e. within session variability). Design and method: We investigated the stability of within session SBP and DBP across 4 datasets by evaluating differences between 3 consecutive inflations recorded 1-2 minutes apart using a clinically validated oscillometric BP monitor that passed the ANSI/AAMI/ISO 2013, ESH-IP 2002 and BHS 1993 protocols (Table 1). These datasets included patients with controlled or uncontrolled hypertension. Results: Data were available from over 10.000 measurement pairs. The differences between inflation 1 and 2 were within ±10, 20 and 30 mmHg for 78.1, 94.4 and 97.9% of the SBP measurements, respectively. For the DBP, 90.7, 95.5 and 97.3 % of the differences between inflation 1 and 2 were within ±10, 15 and 20 mmHg, respectively. A similar trend was seen between inflation 2 and 3. Table 1: Categorical Analysis of BP changes between consecutive inflations. Conclusions: Our data demonstrate substantial variability between consecutive oscillometric BP measurements performed a few minutes apart. Over 20% of the time, consecutive inflations show an absolute change in SBP of at least 10 mmHg. It is unclear whether this represents true physiologic changes in BP or is partly artefactual due to failure of a device's algorithm to determine BP accurately from the oscillometric waveform. These data suggest that criteria for within session variability for automated oscillometric blood pressure devices should be established, including a threshold for repeating the session after 10 minutes of rest.

PS-BPC03-7: ARE OSCILLOMETRIC WAVEFORMS DIFFERENT IN PREGNANCY?

Objective: Hypertensive disorders of pregnancy are leading causes of maternal and perinatal morbidity and mortality. Automated blood pressure (BP) measurements are being used in clinical practice; however, understanding of how oscillometric waveform vary between pregnant and non-pregnant individuals remains low. Design and Methods: Pregnant individuals over 20 weeks gestational age and healthy, non-pregnant women were recruited. Healthy, non-pregnant women (HNP) were matched to healthy pregnant women (HP), and pregnant women with a hypertensive disorder of pregnancy (HDP) by age (± 10 years), arm circumference (± 6 cm), and BMI at the time of BP measurement (± 13 kg/m2). There were seven individuals in each group for a total of 21 participants. BP measurements were done per the International Organization for Standardization (ISO) protocol using a custom-built oscillometric device as the test device and 2-observer mercury auscultation as the reference measurement. Four pairs of auscultatory measurements and four oscillometric measurements were obtained for each participant. Baseline demographics, auscultatory BP and BP derived from slope-based and fixed ratio algorithms were determined. Oscillometric waveform and envelope features were compared among groups. Results: In HNP, HP, and HDP groups respectively: mean age was 31.3 ± 4.8; 32.1 ± 3.6; 32.0 ± 4.4 years; arm circumference 31.9 ± 3.3; 32.3 ± 4.9; 34.0 ± 3.3 cm; BMI 26.8 ± 3.8; 32.0 ± 7.2; 32.4 ± 3.7 kg/m2; mean auscultatory BP (systolic/diastolic) 103.3 ± 11.1/66.5 ± 7.4; 110.6 ± 4.0/56.9 ± 6.5; 132.6 ± 19.0/83.9 ± 13.3 mmHg. HDP had significantly higher auscultatory systolic (P &lt; 0.05) and diastolic (p &lt; 0.01) BP than the other groups. The pregnant groups (HP, HDP) had significantly higher heart rate (p &lt; 0.01); higher pulses (p &lt; 0.05) in the oscillometric waveform (OMV); lower average width in the pulses (p &lt; 0.01); higher pressure amplitude at which the OMW peaks (p &lt; 0.05) with longer time to reach this maximum pressure amplitude (p &lt; 0.05); greater area under the curve (p &lt; 0.01). Compared to HNP, the HDP showed significantly skewed OMW to the right (p &lt; 0.01); greater spread in the OMW envelope (p &lt; 0.05) and lesser random variation in the OMW envelope (p &lt; 0.01). A typical OMV with the 3 groups superimposed is demonstrated in Figure 1. Conclusion: Significant differences in the oscillometric waveform morphology and parameters were found in pregnancy and in hypertensive disorders of pregnancy compared to healthy controls. These results suggest that pregnancy-specific algorithms may be required to optimize accuracy for oscillometric BP measurement.

PS-BPC03-6: USING A SIMPLE FIXED-RATIO BASED ALGORITHM MINIMIZES VARIABILITY IN OSCILLOMETRIC BLOOD PRESSURE DERIVATION IN PATIENTS WITH CHRONIC KIDNEY DISEASE

Objective: Oscillometric device accuracy may be decreased, and variability may be increased, in patients with chronic kidney disease. The objective of this study was to compare three oscillometric algorithms to identify which minimizes oscillometric blood pressure variability in chronic kidney disease. Design &amp; Methods: Sixty patients with chronic kidney disease were studied. Thirty were on hemodialysis (HD group) and thirty had stage 3 chronic kidney disease (Stage 3 CKD). Baseline demographics and medical comorbidities were recorded. The mean of 3 readings from the Omron HEM 907XL device was used to provide a control (reference) blood pressure. Three oscillometric waveforms were also collected using a computer-based system, processed, and used to derive blood pressure by each of three algorithms: 1. simple fixed-ratio; 2. initial binning according to mean arterial pressure and then application of different fixed ratios within each bin (binning method); and 3. binning plus linear regression to incorporate the influence of additional oscillometric waveform characteristics (binning regression method). The mean of three blood pressure determinations from each algorithm was calculated and compared to the Omron mean, with variability assessed by calculating the standard deviation of the difference compared to the Omron. Results: Mean age in the overall group (± SD) was 65.6 ± 13.7 years (HD 63.4 ± 16.3 years; Stage 3 CKD 65.9 ± 10.7 years), percent female in the overall group was 42% (HD 37%; Stage 3 CKD 47%), and mean arm circumference overall was 31.5 ± 4.7 cm (HD 30.8 ± 4.4 cm; Stage 3 CKD 32.2 ± 5.0 cm. Hypertension was present in 82% of participants (HD 80%; Stage 3 CKD 83%). Mean Omron blood pressure was 136.9 ± 25.4/72.7 ± 15.8 mmHg. Compared to the Omron, the systolic/diastolic results for each algorithm were 134.4 ± 20.5/75.0 ± 12.9 for fixed ratio, 128.5 ± 19.8/71.4 ± 12.0 for binning, and 127.7 ± 18.7/70.2 ± 8.8 for binning regression (Table 1). Conclusions: An oscillometric algorithm based on simple fixed-ratio minimized variability in patients with chronic kidney disease. Manufacturers of devices designed for blood pressure measurement in this population should consider incorporating this approach into their algorithm-based blood pressure derivation.

Evaluation of blood pressure using a flexible and wearable capacitive pressure sensor.

In recent times, the high demand for flexible and wearable pressure sensors to monitor human health, particularly for patients afflicted with hypertension or high blood pressure (BP), has captured the keen interest of researchers. Capacitance-based flexible sensing devices offer real-time metrics regarding vital physiological parameters of the human body, such as BP and pulse rate (PR), thereby enabling the identification of cardiovascular complications. In this regard, we have developed a capacitive pressure sensor using polydimethylsiloxane (PDMS) and deionized water (DIW) and improved its key parameters by adding baking powder to PDMS-DIW. The sensor demonstrated excellent performance in static pressure measurements with a sensitivity of 0.021 Pa-1, detection limit of 1 Pa, and response time of 100 ms. We further investigated its application in human BP monitoring. The sensor successfully captured the oscillometric waveform (OMW) for all 160 participants and demonstrated excellent performance in accurately measuring BP, meeting all criteria outlined as the universal standard when compared with the reference devices: OMRON BP device and the gold-standard mercury-based sphygmomanometer. Furthermore, the sensor accurately provided the PR and agreed well with the reference BP device. Therefore, the developed BP sensor can be a viable alternative to replace the pressure sensors in existing BP devices.

DeepPAD: Detection of Peripheral Arterial Disease Using Deep Learning

Peripheral arterial disease (PAD) is a common circulatory disease caused by the deposition of fatty plaque on the arterial wall. Early detection and treatment of PAD are essential in preventing further cardiovascular and health complications. It was recently shown that PAD can be detected using an oscillometric device by characterizing the peripheral arterial system at different externally applied cuff pressures. However, the extraction of the complex relationship between the pattern of the oscillometric waveforms and the presence of PAD remained challenging. This study proposes a novel deep learning approach for the detection of PAD by capturing the peripheral arterial system behavior at different cuff pressures. The periodic pattern of the oscillometric pulses and their variations as a function of cuff pressure were modeled using a deep recurrent neural network based on the bidirectional long short-term memory and attention mechanism. The proposed model was evaluated by analyzing the raw oscillometric pulses as well as statistical features on data collected from 33 individuals (14 PAD and 19 normal). The results show a high accuracy of up to 94.8%, a sensitivity of up to 90.0%, and a specificity of up to 97.4% in detecting PAD. The proposed method provides new opportunities for noninvasive cardiovascular screening and early detection of PAD using the oscillometric principle.

Oscillometric Waveform Evaluation for Blood Pressure Devices

Most of the noninvasive blood pressure (NIBP) device algorithms have been derived from the clinical trials at various hospitals and human races in developed countries. It is urgent to develop an algorithm for NIBP devices for Indian citizens. In this regard, we have developed a flexible pressure sensor to record the human body's oscillometric waveform (OMW). As we know, assessing human blood pressure (BP) by noninvasive techniques is virtually a revolutionary step in biomedical science and engineering. High blood pressure is one of the most critical risk factors for heart disease and death; therefore, continuous monitoring of BP is necessary. We have obtained the BP pulse waveform by the flexible capacitive pressure sensor and further estimated the systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) using international guidelines. In this work, the BP simulation was performed on 64 waveforms, combinations of SBP (80–180 mmHg) and DBP (60–170 mmHg) to compute the BP in different health conditions such as hypotension, normal, and hypertension. In addition, an oscillometric waveform was also obtained from the brachial artery of a healthy person and analyzed for its utility as an NIBP waveform recorder. The SBP, DBP, and MBP were calculated from the oscillometric waveform envelope (OMWE) using the maximum amplitude algorithm (MAA) technique. In-house developed capacitive pressure sensors give comparable results when tested against the NIBP simulator and the commercial BP monitor. The pulse rate is also computed using the same OMW. The mean and standard deviation of the error in SBP, DBP, and MBP are within the universal standards' validation protocol for BP devices. The results indicate that the flexible capacitive pressure sensor can measure the BP with acceptable error and replace the commercial BP monitor and recorder sensor. The developed sensors show potential for large-scale clinical trials for blood pressure and pulse rate measurements.

Blood Pressure Estimation From Korotkoff Sound Signals Using an End-to-End Deep-Learning-Based Algorithm

While measurement of blood pressure (BP) is now widely carried out by automated non-invasive BP (NIBP) monitoring devices, as they do not require skilled clinicians and do not carry risk of complications, their accuracy is in doubt. A novel end-to-end deep learning-based algorithm was developed in this study that estimates NIBP directly from sequences of Korotkoff sounds (KSs) rather than oscillometric waveforms. First, sequences of segments of KSs were formed using different signal segmentation techniques, i.e., segmentation using sliding window with or without overlap and segmentation using the cardiac period estimation. Each segment within each sequence was then labeled as (i) after-systolic and before-diastolic (AB), or (ii) before-systolic or after-diastolic (BA) such that a binary sequence-to-sequence classification problem was achieved. To deal with the resultant sequence-to-sequence classification problem, an algorithm was developed by combining one-dimensional (1D) convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The segments associated with systolic and diastolic blood pressure (SBP and DBP) are then identified as the segments at which the output target sequence switches from class BA to class AB and later from class AB to class BA. Lastly, the values of SBP and DBP are obtained by mapping the center point of the switching segments to the deflation curve. To evaluate the performance of the proposed NIBP estimation method, we used a database of 350 NIBP samples collected from 155 participants (87 male, age: 23-97 years, arm circumference: 10-35 cm, SBP: 81-104 mmHg, and DBP: 37-104 mmHg), and the achieved estimation errors for SBP and DBP, relative to the reference values, using a 5-fold cross validation approach, were 1.6±3.9 mmHg (mean absolute error ± standard deviation of error) and 2.5±4.0 mmHg, respectively. We finally conclude that the proposed end-to-end deep learning-based NIBP estimation algorithm from sequences of KSs is a novel technique that requires modest preprocessing steps and can measure BP accurately.

Oscillometry-Based Blood Pressure Estimation Using Convolutional Neural Networks

Blood pressure measurement is required to monitor the cardiovascular state of a person, and it is commonly conducted in a noninvasive way using oscillometry-based blood pressure monitors (BPM). Blood pressure can be estimated by analyzing the oscillometric waveform (OMW) in the BPM, and many methods have been examined to increase their estimation accuracy. In this study, we proposed a new method that enhances estimation accuracy and requires no external user information, such as age and gender, in the test phase. In the method, the entire OMW was considered as an input to reduce information loss via feature extraction, and convolutional neural networks were utilized to effectively analyze the high-dimensional input. Additionally, the proposed method included a novel ensemble method to further increase the estimation accuracy. The performance of the proposed method was evaluated and compared with other studies via subject-independent tests considering real situations in which it is difficult to obtain preliminary information on a test subject. Data from 64 subjects were used in the test. The mean absolute error of the proposed method was 3.12 and 3.98 mmHg for systolic and diastolic blood pressure, respectively, which was superior to those reported in other studies conducted in similar conditions. Individuals can measure their blood pressure with higher precision using the proposed method with improved estimation performance. This can aid in reducing the risk of cardiovascular diseases.

Influence of aging and increased blood pressure on oscillometric cuff pressure waveform characteristics.

The oscillometric blood pressure (BP) measurement technique estimates BPs from analyzing the envelop of oscillometric cuff pressure waveform. The oscillometric waveform envelope shape is associated with physiological changes and influences BP measurement accuracy. The aim of this study was to comprehensively quantify BP- and age-related changes of oscillometric waveform envelope characteristics. Manual systolic and diastolic BPs were measured from 472 patients (219 female, 253 male), and the cuff pressure were digitally recorded during linear cuff deflation that was used to derive oscillometric waveform envelopes. All patients were divided into different categories according to their BP level and age. The envelope width in high-pressure region (above mean arterial pressure, MAP) and low-pressure region (below MAP) were compared between different BP and age categories to qualify their changes with increased BP and aging. The envelop widths increased significantly with increased BPs (P < 0.001 between optimal, normotensive and hypertensive groups) and aging (P < 0.001 for >50 years old group in comparison with younger groups). The envelope widths in high-pressure region were significantly larger than in low-pressure region in normal and hypertensive categories (all P < 0.05) and elderly patients aged over 60 years (all P < 0.001), and the envelope width ratios between them increased with increased BP and aging. This study has concluded an asymmetrical oscillometric waveform envelope in normotensive and hypertensive categories, as well as in elderly group (aged over 60 years), and their asymmetrical features were significantly more obvious with increased BP and aging.

Flexible and wearable capacitive pressure sensor for blood pressure monitoring

A flexible pressure sensor based on a capacitive transduction mechanism having a polydimethylsiloxane (PDMS) layer incorporated between indium‐‑tin-oxide (ITO) coated flexible polyethylene terephthalate (PET) electrodes has been developed. The sensor's key parameters have been improved by increasing the dielectric layer's porosity by introducing deionized water (DIW) into it. The sensing device with a porous dielectric layer (PDMS-DIW) exhibits a 0.07%–15% relative difference in capacitance for the applied pressure range from 1 Pa to 100 kPa. The device also demonstrates improved sensitivity compared to a PDMS layer (unstructured) throughout the complete external pressure range. The device with porous PDMS layer shows an extensive operating pressure range (1 Pa to 100 kPa), high working stability, quick response (≈110 ms) and ultra-low detection limit of pressure, i.e., 1 Pa. In addition, the blood pressure monitoring was also studied, and the devices gave a signature of the oscillometric waveform for different blood pressure (BP) values. The fabricated flexible pressure sensor can be used for wearable BP devices and biological applications due to its excellent functional properties.

Retracted] A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things

The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age‐related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long‐term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real‐time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signals. It was proposed to automatically remove outlier points generated by movement artefacts from the blood pressure signal. Next, eleven features taken from the oscillometric waveform envelope were utilised to analyse the relationship between diastolic blood pressure (SBP) and systolic blood pressure (DBP). This paper validates a proposed computational method for estimating blood pressure. The proposed architecture leverages sophisticated regression to predict systolic and diastolic blood pressure values from PPG signal characteristics.

Cardio-ankle vascular index as an arterial stiffness marker improves on cardiovascular events by adding to framingham risk score

Abstract Background The cardio-ankle vascular index (CAVI) is a non-invasive measurement that evaluates arterial stiffness using the analysis of oscillometric waveform during cuff-Inflation. Several studies reported that CAVI is associated with cardiovascular risk factors, while the clinical prognostic value of CAVI as a surrogate marker of atherosclerosis has not been fully elucidated. Meanwhile, the Framingham risk score (FRS) is an established marker of cardiovascular outcomes. Purpose To investigate whether adding CAVI to Framingham risk score improves the prediction of cardiovascular events. Methods This prospective observational study included consecutive 422 patients with cardiovascular risk factors but without known coronary artery disease (69±8 years, 63% men). CAVI was measured by the oscillometric method with VaSera vascular screening system. Patients with atrial fibrillation, left ventricular ejection fraction &amp;lt;50%, both ABI&amp;lt;0.9, severe valvular diseases, or hemodialysis were excluded. Primacy outcomes were cardiovascular death, myocardial infarction, stroke, hospitalization for heart failure and revascularization. Results During a median follow-up of 3.1 years, cardiovascular events occurred in 12.8% (3.3%, 15.7%, and 19.1% in the low, intermediate and high-risk group of stratification by FRS, respectively). The ROC curve analysis for discriminating cardiovascular events showed that the AUC of CAVI added to Framingham risk score was the highest compared to Framingham risk score and CAVI alone (CAVI added to Framingham risk score: AUC 66.9, 95% CI 59.6–74.2, Framingham risk score alone: AUC 61.5, 95% CI 53.8–69.1, CAVI alone: AUC 62.3, 95% CI 54.1–70.6). The logistic regression analysis demonstrated that CAVI and Framingham risk score were independent predictors of cardiovascular events (CAVI: OR 1.381, 95% CI 1.164–1.597, p=0.004, Framingham risk score: OR 1.135, 95% CI 1.044–1.225, p=0.007). Next, when logistic regression analysis was performed simultaneously on Framingham risk factor and CAVI, CAVI was an independent predictor of cardiovascular events (OR 1.347, 95% CI 1.124–1.569, p=0.009). Furthermore, in the likelihood ratio test, CAVI added to Framingham risk score significantly improved the cardiovascular event prediction ability than Framingham risk factor alone. Next, when patients with intermediate risk (n=217) were divided into two groups based on CAVI of 9.0, the Kaplan-Meier estimate showed that events occurred more frequently in higher CAVI group (9.3% and 29.1%, log-rank, P=0.009) and the C-statistic was 0.662. Multiple Cox analysis showed that, in the intermediate risk group, CAVI was an independent predictor of primary outcomes (HR 1.387 per 1 index, 95% CI 1.081–1.779, p=0.010). Conclusion The measurement of CAVI could be a useful predictor for cardiovascular events. In addition, the combination of CAVI and Framingham risk score could improve the predictability compared to the Framingham risk score alone. Funding Acknowledgement Type of funding source: None

Optimum waveform envelopes and amplitude ratios in oscillometric blood pressure estimation.

To determine if, when using the oscillometric method, there is a specific range of amplitude ratios in the fixed-ratio algorithm that will result in blood pressure estimates that consistently fall within a mean error ≤5 mmHg and a SD of the error <8 mmHg. Additionally, to apply different representations of the oscillometric waveform envelope to verify if this will affect the accuracy of the results. SBP and DBP were obtained using the fixed-ratios method applied to a dataset of 219 oscillometric measurements obtained from 73 healthy volunteers and compared to their corresponding auscultation values. Ratio and envelope analysis were done on Matlab (The MathWorks, Inc., Natick, Massachusetts, USA). Depending on the envelope representation, ratios between 0.44-0.74 for systolic pressure and 0.51-0.85 for diastolic pressure yield results within the limits mentioned above. When a set of optimum envelope representations and ratios are selected based on population mean, the highest percentage of subjects presenting blood pressure estimates within the limits were 72.6% for systolic and 69.9% for diastolic. The range of ratios presenting optimum results appears to be independent of the degree of arterial stiffness given the wide range of ages of the subjects in the study. Different representations of the oscillometric waveform envelope may improve the accuracy of the method. However, there remains a considerable percentage of the population with unreliable results. It is therefore important to only use devices that have been properly validated according to standard protocol.

A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods

Blood pressure measurement and prediction is an important condition for heart patients and people with heart problems and should be kept under constant control. In this study, based on the oscillometric waveform obtained from individuals using a cuff, the oscillometric waveforms are divided into three periods. These periods are; the first period from the starting point to the systolic blood pressure (SBP), the second period is between systolic blood pressure (SBP) and diastolic blood pressure (DBP), and the third period is between diastolic blood pressure (DBP) and end of the waveform. In the dataset used, the attributes obtained from the oscillometric wave envelope were subtracted for each pulse. On the dataset, the attributes of the beat corresponding to the systolic pressure point are labeled 1, and the attributes of the beat corresponding to the diastolic pressure point are labeled with 2. Other beats are labeled with 0. In the study, the dataset was first re-labeled. Systolic beats were labeled with 1, beats before systolic point, 2 with systolic, diastolic point including diastolic point, and 3 with a diastolic point. After re-labeling, 350 measurements, 300 measurements were divided into training and 50 measurements were divided into test data subset. Classifiers were trained with 300 subsets and the classifier model was produced. With the generated model, the classification of the pulse sequences in the test data subsets was performed. In the found label series, the first 1 to 2 label was marked as the systolic pressure point and the last 2 to 3 labels as the diastolic pressure point and the corresponding cuff pressures were estimated as systolic and diastolic pressure values. By classifying these periods, the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values have been estimated using three classifier algorithms including k-nearest neighbor (kNN), weighted k-nearest neighbor (WkNN), and Bagged Trees algorithms. To evaluate the performance of the prediction algorithms, four different performance metrics comprising MAE (mean absolute error), MSE (mean square error), RMSE (root mean square error), and R2 have been used. For the estimation of SBP values using the kNN algorithm, weighted kNN, and Bagged Trees, the obtained MAEs are 3.590, 3.520, and 4.499, respectively. As for the estimation of DBP values using kNN algorithm, weighted kNN and, Bagged Trees, the obtained MAEs are 11.077, 11.032, and 13.069, respectively. The obtained results demonstrated that the proposed method could be used in the blood pressure estimation as the new approach.