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.

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Frequent blood pressure (BP) assessment is key to the diagnosis and treatment of many severe diseases, such as heart failure, kidney failure, hypertension, and hemodialysis. Current "gold-standard'' BP measurement techniques require the complete blockage of blood flow, which causes discomfort and disruption to normal activity when the assessment is done repetitively and frequently. Unfortunately, patients with hypertension or hemodialysis often have to get their BP measured every 15 minutes for a duration of 4-5 hours or more. The discomfort of wearing a cumbersome and limited mobility device affects their normal activities. In this work, we propose a device called eBP to measure BP from inside the user's ear aiming to minimize the measurement's impact on users' normal activities while maximizing its comfort level. eBP has 3 key components: (1) a light-based pulse sensor attached on an inflatable pipe that goes inside the ear, (2) a digital air pump with a fine controller, and (3) a BP estimation algorithm. In contrast to existing devices, eBP introduces a novel technique that eliminates the need to block the blood flow inside the ear, which alleviates the user's discomfort. We prototyped eBP custom hardware and software and evaluated the system through a comparative study on 35 subjects. The study shows that eBP obtains the average error of 1.8 mmHg and -3.1 mmHg and a standard deviation error of 7.2 mmHg and 7.9 mmHg for systolic (high-pressure value) and diastolic (low-pressure value), respectively. These errors are around the acceptable margins regulated by the FDA's AAMI protocol, which allows mean errors of up to 5 mmHg and a standard deviation of up to 8 mmHg.

eBP: A Wearable System For Frequent and Comfortable Blood Pressure Monitoring From User's Ear

Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

The arterial blood pressure is an essential physiological parameter for health monitoring. Most blood measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This method is uncomfortable to the user and may cause anxiety which in turns can affect the blood pressure (white coat syndrome). This paper investigates a cuff-less non-intrusive approach to measure arterial blood pressure that is suitable for continuous measurement. The approach is based on measuring the delay between the R-peak of the electrocardiogram (ECG) signal and the peaks of the finger photoplethysmograph (PPG) signal. The results of this paper show a high correlation between the blood pressure and the pulse transit time (PTT). Different polynomial regressions are applied for further estimation. The paper uses actual ECG, PPG and blood pressure measurements extracted from the MIMIC database that contains clinical signal data reflecting real measurements. The simulation results verify that the delay (PTT) between the R-peak of the ECG signal and the peaks of the finger PPG signal have a high correlation with arterial blood pressure and can be used as an indicator of the arterial blood pressure.

Evaluation of the correlation between blood pressure and pulse transit time

This article presents a novel method for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from time-domain features extracted from oscillometric waveforms (OWs) using a long short-term memory (LSTM) recurrent neural network (RNN) method. First, we extract seven time-domain features from each cycle of OW, including the cuff pressure, the cardiac period, the trough-to-peak amplitude of OW, the time between the trough and the peak of the OW, the slopes of the oscillometric waveform envelope (OWE), and the maximum upslope of individual OWs. Second, we locate each feature vector in an noninvasive blood pressure (NIBP) record in one of three different phases (classes), namely, presystolic (PS), between systolic and diastolic (BSD), and after diastolic (AD), and form a target sequence. Then, we propose an LSTM-RNN approach to effectively learn the complex nonlinear relationship between the feature vector sequences and the target sequence. 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 tenfold cross-validation scheme and using a database of 350 NIBP recordings gave an average mean error of −1.2 ± 5.9 mmHg for SBP and 1.8 ± 8.8 mmHg for DBP relative to reference values derived from a visual method of determining SBP and DBP. The proposed RNN-based approach uses all time-domain features available from each NIBP recording and can outperform traditional methods in blood pressure (BP) estimation.

Blood Pressure Estimation From Time-Domain Features of Oscillometric Waveforms Using Long Short-Term Memory Recurrent Neural Networks

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 measurements are used to demonstrate the feasibility of the proposed approach. The estimates obtained through pulse morphology-based approach is compared with those obtained from a commercial blood pressure device.

/pdf/blood-pressure-estimation-using-oscillometric-pulse-4iu8stqby1.pdf

Blood pressure estimation using oscillometric pulse morphology

A new oscillometric pulse index (OPI) derived from the maximum slope (MS) of each pulse in the oscillometric blood pressure waveform is proposed for blood pressure estimation Maximum slope for each pulse is obtained using the first derivative of the pulse and an envelope of the values corresponding to the maximum slopes is obtained The maximum of the envelope is taken as the mean arterial pressure (MAP) and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates are obtained as a fraction of the MAP, similar to the traditional maximum amplitude algorithm (MAA) The proposed algorithm is tested on 18 healthy subjects The MAP, SBP and DBP estimates obtained from the proposed algorithm are compared with those obtained from a commercial blood pressure device and with the estimates obtained using the MAA and morphological qualitative measures available in the literature

Blood pressure estimation using maximum slope of oscillometric pulses

This paper presents a new analysis of oscillometric blood pressure signals using pulse morphology under different pressure points. As the pulse morphology contains potentially critical clinical information, quantitative signal metrics are used to characterize the blood pressure pulse using pulse morphology. Signals metrics at three different pressure points namely systolic, mean arterial and diastolic pressure points are obtained and are compared across three different age groups (&#60;30, 30–55, >55). It is observed that the pulses change their characteristics at different pressure points and also with age. This preliminary analysis indicates the usefulness of pulse morphology of oscillometric blood pressure signals for diagnostic purposes through comparison of the proposed signal metrics over age and at different pressure points.

Oscillometric blood pressure pulse morphology

This paper investigates a high-quality and multi-purposed biosensor with maximum stable output transmittance numerically by using the inverse design method. The proposed biosensor utilises particle swarm optimisation for inverse design which will be a helpful way of designing different kinds of precise sensors in the future. In this research, some parameters are introduced to the optimiser to find the best cavity parameters for developing a high-quality sensor to sense different targets. Many previous studies were on single-goal biosensors, or their quality factor and output spectrum were very low. The proposed sensor can sense different parts of blood components, the amount of glucose in the urine, and tear's glucose for the first time just in one device to the best of our knowledge. Compared to previous works, this structure detects the differences between refractive indexes analytes with a high-quality factor and a high and stable output transmittance spectrum. This structure contains two-dimensional photonic crystal microresonators to provide resonance frequencies in the photonic bandgap. The device works on a window of 1.55 μm with a quality factor equal to 24,000, the sensitivity is 500 nm/RIU (refractive index unit), and the resolution is equal to 4 × 10−5. In this paper, the scaling method, particle swarm optimisation, two-dimensional finite-difference dime domain, and Plane-Wave Expansion methods are utilised. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ote2.12066

Inverse design of a high‐quality factor multi‐purpose optical biosensor

Background: Ascending thoracic aortic aneurysm (ATAA) is an asymptomatic localized dilation of the aorta that is prone to rupture with a high rate of mortality. While diameter is the main risk factor for rupture assessment, it has been shown that the peak wall stress from finite element (FE) simulations may contribute to refinement of clinical decisions. In FE simulations, the intraluminal boundary condition is a single-phase blood flow that interacts with the thoracic aorta (TA). However, the blood is consisted of red blood cells (RBCs), white blood cells (WBCs), and plasma that interacts with the TA wall, so it may affect the resultant stresses and strains in the TA, as well as hemodynamics of the blood. Methods: In this study, discrete elements were distributed in the TA lumen to represent the blood components and mechanically coupled using fluid–structure interaction (FSI). Healthy and aneurysmal human TA tissues were subjected to axial and circumferential tensile loadings, and the hyperelastic mechanical properties were assigned to the TA and ATAA FE models. Results: The ATAA showed larger tensile and shear stresses but smaller fluid velocity compared to the ATA. The blood components experienced smaller shear stress in interaction with the ATAA wall compared to TA. The computational fluid dynamics showed smaller blood velocity and wall shear stress compared to the FSI. Conclusions: This study is a first proof of concept, and future investigations will aim at validating the novel methodology to derive a more reliable ATAA rupture risk assessment considering the interaction of the blood components with the TA wall.

/pdf/interaction-of-the-blood-components-with-ascending-thoracic-3bqwr88m.pdf

Interaction of the Blood Components with Ascending Thoracic Aortic Aneurysm Wall: Biomechanical and Fluid Analyses

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Majid Mafi

Author Tools

Chat about Author