Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement

Survey and Evaluation of Hypertension Machine Learning Research

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Variation of peripheral pulse transit time with internal vascular pressure changes induced by arm movement

Pulse transit time (PTT) and blood pressure (BP) are widely used to quantify arterial characteristics. Arm position influences arterial BP and peripheral PTT. This study aims to quantify the relationship between PTT changes with internal vascular pressure variations induced by the arm moving. With left arm at horizontal position as reference and the right arm moving from 90 to 45, 0, -45, and -90° respectively, PTT difference was calculated by the difference of the pulse foot between right arm and left arm within the same heartbeat. The change in the BP was calculated from the gravitational effect with the measured arm length. Our results showed that the change in PTT with arm elevating is more obvious than that with arm lowering, indicating the different relationship between PTT changes due to the internal BP changes. This can help in understanding the inherent physiological/pathological mechanism of cardiovascular system.

Sources of automatic office blood pressure measurement error: a systematic review

OBJECTIVE

Accurate and reliable blood pressure (BP) measurement is important for the prevention and treatment of hypertension. The oscillometric-based automatic office blood pressure measurement (AOBPM) is widely used in hospitals and clinics, but measurement errors are common in BP measurements. There is a lack of systematic review of the sources of measurement errors.

APPROACH

A systematic review of all existing research on sources of AOBPM errors. A search strategy was designed in six online databases, and all the literature published before October 2021 was selected. Those studies that used the AOBPM device to measure BP from the upper arm of subjects were included.

MAIN RESULTS

A total of 1365 studies were screened, and 224 studies were included in this final review. They investigated 22 common error sources with clinical AOBPM. Regarding the causes of BP errors, this review divided them into the following categories: the activities before measurement, patient's factors, measurement environment, measurement procedure, and device settings. 13 sources caused increased systolic and diastolic BP (SBP and DBP), 2 sources caused the decrease in SBP and DBP, only 1 source had no significant effect on BPs, and the other errors had a non-uniform effect (either increase or decrease in BPs). The error ranges for SBP and DBP were -14 to 33 mmHg and -6 to 19 mmHg, respectively.

SIGNIFICANCE

The measurement accuracy of AOBPM is susceptible to the influence of measurement factors. Interpreting BP readings need to be treated with caution in clinical measurements. This review made comprehensive evidence for the need for standardized BP measurements and provided guidance for clinical practitioners when measuring BP with AOBPM devices.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study

OBJECTIVE

The aim of this study was to evaluate the performance of using a deep learning-based method for measuring SBPs and DBPs and the effects of cuff inflation and deflation rates on the deep learning-based blood pressure (BP) measurement (in comparison with the manual auscultatory method).

METHODS

Forty healthy subjects were recruited. SBP and DBP were measured under four conditions (i.e. standard deflation, fast deflation, slow inflation and fast inflation) using both our newly developed deep learning-based method and the reference manual auscultatory method. The BPs measured under each condition were compared between the two methods. The performance of using the deep learning-based method to measure BP changes was also evaluated.

RESULTS

There were no significant BP differences between the two methods (P > 0.05), except for the DBPs measured during the slow and fast inflation conditions. By applying the deep learning-based method, SBPs measured from fast deflation, slow inflation and fast inflation decreased significantly by 3.0, 3.5 and 4.7 mmHg (all P < 0.05), respectively, in comparison with the standard deflation condition. Whereas, corresponding DBPs measured from the slow and fast inflation conditions increased significantly by 5.0 and 6.8 mmHg, respectively (both P < 0.05). There were no significant differences in BP changes measured by the two methods in most cases (all P > 0.05, except for DBP change in the slow and fast inflation conditions).

CONCLUSION

This study demonstrated that the deep learning-based method can achieve accurate BP measurement under the deflation and inflation conditions with different rates.