Blood Pressure Assessment with Differential Pulse Transit Time and Deep Learning: A Proof of Concept

Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

Wearable Two-Dimensional Nanomaterial-Based Flexible Sensors for Blood Pressure Monitoring: A Review

Flexible sensors have been extensively employed in wearable technologies for physiological monitoring given the technological advancement in recent years. Conventional sensors made of silicon or glass substrates may be limited by their rigid structures, bulkiness, and incapability for continuous monitoring of vital signs, such as blood pressure (BP). Two-dimensional (2D) nanomaterials have received considerable attention in the fabrication of flexible sensors due to their large surface-area-to-volume ratio, high electrical conductivity, cost effectiveness, flexibility, and light weight. This review discusses the transduction mechanisms, namely, piezoelectric, capacitive, piezoresistive, and triboelectric, of flexible sensors. Several 2D nanomaterials used as sensing elements for flexible BP sensors are reviewed in terms of their mechanisms, materials, and sensing performance. Previous works on wearable BP sensors are presented, including epidermal patches, electronic tattoos, and commercialized BP patches. Finally, the challenges and future outlook of this emerging technology are addressed for non-invasive and continuous BP monitoring.

Assessing systemic vascular resistance using arteriolar pulse transit time based on multi-wavelength photoplethysmography

OBJECTIVE

Sympathetic nerve activity affects blood pressure by contracting the arteriole, which can increase systemic vascular resistance (SVR). Consequently, SVR is a key factor affecting blood pressure. However, a method for measuring SVR continuously is lacking. This paper formulated and experimentally validated a method that uses the arteriolar pulse transmit time (aPTT) to track changes in SVR.

APPROACH

multi-wavelength photoplethysmogram (PPG), electrocardiogram (ECG), and galvanic skin response (GSR) data were simultaneously gathered using a measurement system designed by this study. Blood perfusion was monitored by Laser Doppler. Least mean square (LMS) is an adaptive filtering algorithm. Our LMS-based algorithm formulated in this study was used to calculate the aPTT from the multi-wavelength PPGs. A cold stimulation experiment was conducted to verify the relationship between aPTT determined by algorithm and arteriole vasodilation. An emotinal stimulation experiment conducted, in which GSR was employed to further verify the relationship between aPTT and SVR. Twenty healthy young participants were asked to watch movie clips, which excited their sympathetic nerves. The dynamic time warping (DTW) distance is applied to evaluate between correlation of GSR and aPTT.

MAIN RESULTS

The changes in aPTT was extracted using our LMS-based method. During the recovery period after cold stimulation, aPTT decreased with the average slope of -0.19, while blood perfusion increased with the average slope of 0.72. Meanwhile, 70% participants' DTW distance's median between aPTT and GSR were significantly smaller than that between PTT and GSR during emotion stimulation.

SIGNIFICANCE

Our method uses aPTT, a continuous measurable parameter, to closely reflect SVR, as verified through experiments.

Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection

The cuffless blood pressure (BP) measurement allows for frequent measurement without discomfort to the patient compared to the cuff inflation measurement. With the availability of a large dataset containing physiological waveforms, now it is possible to use them through different learning algorithms to produce a relationship with changes in BP. In this paper, a novel cuffless noninvasive blood pressure measurement technique has been proposed using optimized features from electrocardiogram and photoplethysmography based on multivariate symmetric uncertainty (MSU). The technique is an improvement over other contemporary methods due to the inclusion of feature optimization depending on both linear and nonlinear relationships with the change of blood pressure. MSU has been used as a selection criterion with algorithms such as the fast correlation and ReliefF algorithms followed by the penalty-based regression technique to make sure the features have maximum relevance as well as minimum redundancy. The result from the technique was compared with the performance of similar techniques using the MIMIC-II dataset. After training and testing, the root mean square error (RMSE) comes as 5.28 mmHg for systolic BP and 5.98 mmHg for diastolic BP. In addition, in terms of mean absolute error, the result improved to 4.27 mmHg for SBP and 5.01 for DBP compared to recent cuffless BP measurement techniques which have used substantially large datasets and feature optimization. According to the British Hypertension Society Standard (BHS), our proposed technique achieved at least grade B in all cumulative criteria for cuffless BP measurement.

Towards a portable-noninvasive blood pressure monitoring system utilizing the photoplethysmogram signal

Blood pressure (BP) responds instantly to the body's conditions, such as movements, diseases or infections, and sudden excitation. Therefore, BP monitoring is a standard clinical measurement and is considered one of the fundamental health signs that assist in predicting and diagnosing several cardiovascular diseases. The traditional BP techniques (i.e. the cuff-based methods) only provide intermittent measurements over a certain period. Additionally, they cause turbulence in the blood flow, impeding the continuous BP monitoring, especially in emergency cases. In this study, an instrumentation system is designed to estimate BP noninvasively by measuring the PPG signal utilizing the optical technique. The photoplethysmogram (PPG) signals were measured and processed for ≈ 450 cases with different clinical conditions and irrespective of their health condition. A total of 13 features of the PPG signal were used to estimate the systolic and diastolic blood pressure (SBP and DBP), utilizing several machine learning techniques. The experimental results showed that the designed system is able to effectively describe the complex-embedded relationship between the features of the PPG signal and BP (SBP and DBP) with high accuracy. The mean absolute error (MAE) ± standard deviation (SD) was 4.82 ± 3.49 mmHg for the SBP and 1.37 ± 1.65 mmHg for the DBP, with a mean error (ME) of ≈ 0 mmHg. The estimation results are consistent with the Association for the American National Standards of the Association for the Advancement of Medical Instrumentation (AAMI) and achieved Grade A in the British Hypertension Society (BHS) standards for the DBP and Grade B for the SBP. Such a study effectively contributes to the scientific efforts targeting the promotion of the practical application for providing a portable-noninvasive instrumentation system for BP monitoring purposes. Once the BP is determined with sufficient accuracy, it can be utilized further in the early prediction and classification of various arrhythmias such as hypertension, tachycardia, bradycardia, and atrial fibrillation (as the early detection can be a critical issue).

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.

Cuffless Blood Pressure Monitoring: Promises and Challenges

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.