Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic

Cuffless and Touchless Measurement of Blood Pressure from Ballistocardiogram Based on a Body Weight Scale

Currently, in terms of reducing the infection risk of the COVID-19 virus spreading all over the world, the development of touchless blood pressure (BP) measurement has potential benefits. The pulse transit time (PTT) has a high relation with BP, which can be measured by electrocardiogram (ECG) and photoplethysmogram (PPG). The ballistocardiogram (BCG) reflects the mechanical vibration (or displacement) caused by the heart contraction/relaxation (or heart beating), which can be measured from multiple degrees of the body. The goal of this study is to develop a cuffless and touchless BP-measurement method based on a commercial weight scale combined with a PPG sensor when measuring body weight. The proposed method was that the PTTBCG-PPGT was extracted from the BCG signal measured by a weight scale, and the PPG signal was measured from the PPG probe placed at the toe. Four PTT models were used to estimate BP. The reference method was the PTTECG-PPGF extracted from the ECG signal and PPG signal measured from the PPG probe placed at the finger. The standard BP was measured by an electronic blood pressure monitor. Twenty subjects were recruited in this study. By the proposed method, the root-mean-square error (ERMS) of estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 6.7 ± 1.60 mmHg and 4.8 ± 1.47 mmHg, respectively. The correlation coefficients, r2, of the proposed model for the SBP and DBP are 0.606 ± 0.142 and 0.284 ± 0.166, respectively. The results show that the proposed method can serve for cuffless and touchless BP measurement.

Basic technology and proper usage of home health monitoring devices

Home health monitoring devices are consumer-grade devices that help to monitor the health of individuals at home. These devices are usually low-cost and easily procurable, and they can be operated by patients or their caretakers with minimal training. However, improper usage of these devices may provide erroneous results, which can lead to an unnecessary hospital visit or teleconsultation. In this article, we discuss the basic technology and proper usage of some of these devices, namely automatic blood pressure monitors, blood glucose monitors, body fat monitors, pulse oximeters, electrocardiographs, digital thermometers, and infrared thermometers. This brief document intends to help primary health care professionals and their patients use these devices.

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review

Cardiovascular disease is the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as measurements can be taken by patients at home. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.

Novel methods of testing and calibration of oscillometric blood pressure monitors

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.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.

An integrated blood pressure measurement system for suppression of motion artifacts

Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. Motion artifacts are considered external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibrations. In the proposed work, a new algorithmic stage is integrated in a non-invasive BP monitor. This stage suppresses the effect of the motion artifact and adjusts the pressure estimation before displaying it to users. The proposed stage is based on a 3-axis accelerometer signal, which helps in the accurate detection of the motion artifact. Both transient motion artifacts and artifact due to vibrations are suppressed using algorithms based on Empirical Mode Decomposition (EMD). Measurements with human subjects show that the proposed algorithms considerably improved the accuracy of the blood pressure estimates in comparison with the commonly-used conventional oscillometric algorithm that does not include an EMD-based stage for artifact suppression, and allowed the estimates to meet the requirements of the international ANSI/AAMI/ISO standard.

On using maximum a posteriori probability based on a Bayesian model for oscillometric blood pressure estimation

The maximum amplitude algorithm (MAA) is generally utilized in the estimation of the pressure values, and it uses heuristically obtained ratios of systolic and diastolic oscillometric amplitude to the mean arterial pressure (known as systolic and diastolic ratios) in order to estimate the systolic and diastolic pressures. This paper proposes a Bayesian model to estimate the systolic and diastolic ratios. These ratios are an improvement over the single fixed systolic and diastolic ratios used in the algorithms that are available in the literature. The proposed method shows lower mean difference (MD) with standard deviation (SD) compared to the MAA for both SBP and DBP consistently in all the five measurements.

Mathematical biomarkers for the autonomic regulation of cardiovascular system

Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.

Oscillometric blood pressure monitor modeling

Oscillometric blood pressure monitors are fairly reliable medical equipment, inexpensive and widely used both in domicile and clinical measurements of blood pressure, however they have not yet been subject of deep investigation. The research reported in this paper presents the development of an analytical model of an oscillometric blood pressure monitor, a task which has not been dealt with and is useful in order to improve these devices' accuracy limitations. The approach taken was to divide the transducer in two distinct components, the electromechanical and the pneumatic, considering compression and decompression phases. Differential equations were derived, using electric and mechanical principles, to explain the equipment's behavior during the air-pump compression, and the pressure evolution during decompression was identified by exponential approximation. This comprehensive study obtained several non-ideal and nonlinear dynamics, and describes some of the possible simplifications to the models used.

A new oscillometry-based method for estimating the brachial arterial compliance under loaded conditions

We propose a new method for assessing the compliance of a compressed brachial artery using an oscillometry-based approach that is mathematically based on artery and air-cuff models. The cuff dynamics during the inflation period were characterized by simultaneously recording the cuff volume and internal pressure with a pressure transducer and an airflow meter, respectively, which yielded the envelope of the oscillation amplitudes (OAs) in the air cuff. This allowed the change in the arterial volume during each heartbeat at different cuff pressures to be calculated, yielding a changed volume-pressure curve. The oscillometry-derived loaded compliance of the brachial artery (Cosci) can be determined as the dynamic changed volume divided by the pulse pressure. Furthermore, we developed a direct scheme to calibrate the calculated dynamic changed volume. In addition, the proposed C(osci) was validated by comparing it with the compliance of the brachial artery (Cecho) estimated echocardiographically from the brachial arterial blood flow in 32 patients whose lower limbs exhibited numbness or lack of strength. The results showed that Cosci and Cecho were significantly correlated between the cuff pressures levels and the mean arterial pressure, systolic pressure, and diastolic pressure (r=0.616, 0.571, and 0.666, respectively; p<0.0001). This suggests that a useful measure of the loaded compliance can be derived from the pattern of the OA waveform in addition to oscillometry-based blood pressure measurements.

Preliminary study of motion artifact rejection for NIBP measurement in an ambulance

Motion artifact resulting from patient's movement is a significant source for disturbing accurate noninvasive blood pressure (NIBP) measurement. In an ambulance, patients are exposed to unstable circumstances due to vehicle's movement and vibration during emergency transportation. Since NIBP is indirectly measured using oscillometry based on the change of cuff pressure, it can be affected by motion artifact much more than other biosignals. In this paper, we developed a new NIBP system with improved accuracy by measuring acceleration of the system caused by patient's motion. The NIBP module including a 3-axis accelerometer was directly mounted on a cuff to minimize the interference induced through connecting tube. The results show that the proposed NIBP system has the capability to reject the interference of motion artifact effectively in an ambulance.

The problem of artifacts in patient monitor data during surgery: a clinical and methodological review

Artifacts are a significant problem affecting the accurate display of information during surgery. They are also a source of false alarms. A secondary problem is the inadvertent recording of artifactual and inaccurate information in automated record keeping systems. Though most of the currently available patient monitors use techniques to minimize the effect of artifacts, their success is limited. We reviewed the problem of artifacts affecting patient monitor data during surgical cases. Methods adopted by currently marketed patient monitors to eliminate and minimize artifacts due to technical and environmental factors are reviewed and discussed. Also discussed are promising artifact detection and correction methods that are being investigated. These might be used to detect and eliminate artifacts with improved accuracy and specificity.

Application of fuzzy functions for visual presentation of medical data

While interpretation of medical data is very often an ambiguous process, computers usually display results of recommendations, provided by both human experts and computer algorithms, as concrete data. This study proposes a visual presentation of relative degrees of uncertainty along with "standard" concrete medical data. A medical parameter (mean arterial pressure) is dynamically evaluated during surgery for being too low or too high. Fuzzy membership functions are utilized to display degrees of deviation in the form of a clear and concise pie chart. Thus, in the Operating Room the anesthesiologist can be provided with an easy statistical assessment of uncertainty of existing recommendations.