Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure

Hypertension or high blood pressure is a leading cause of death throughout the world and a critical factor for increasing the risk of serious diseases, including cardiovascular diseases such as stroke and heart failure. Blood pressure is a primary vital sign that must be monitored regularly for the early detection, prevention and treatment of cardiovascular diseases. Traditional blood pressure measurement techniques are either invasive or cuff-based, which are impractical, intermittent, and uncomfortable for patients. Over the past few decades, several indirect approaches using photoplethysmogram (PPG) have been investigated, namely, pulse transit time, pulse wave velocity, pulse arrival time and pulse wave analysis, in an effort to utilise PPG for estimating blood pressure. Recent advancements in signal processing techniques, including machine learning and artificial intelligence, have also opened up exciting new horizons for PPG-based cuff less and continuous monitoring of blood pressure. Such a device will have a significant and transformative impact in monitoring patients’ vital signs, especially those at risk of cardiovascular disease. This paper provides a comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations

Algoritmo para el análisis en conjunto de las señales del ECG y PPG

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram’s PPG & Electrocardiogram’s ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG’s morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time–frequency filtered.  Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility.El objetivo de esta investigación consiste en identificar aquellos parámetros provenientes de las señales del electrocardiograma ECG y fotopletismograma PPG que permitan hacer una evaluación de la presión sanguínea utilizando un dispositivo móvil. El método propuesto incluye innovaciones en el procesamiento y análisis de las señales. Con el objetivo de aumentar la precisión de la medición de la presión sanguínea, en este trabajo, se propone la utilización de parámetros provenientes de la señal del PPG en conjunto con el PTT obtenido de las señales del ECG y PPG analizadas en conjunto. Adicionalmente, se propone el diseño e implementación de una red neural para determinar la relación existente entre la presión sanguínea estimada por el método y la de referencia, lo cual permite evaluar la viabilidad del método propuesto

Cuffless ambulatory blood pressure measurement using the photoplethysmogram and the electrocardiogram

Blood pressure (BP), as with other vital signs such as heart rate and respiratory rate, exhibits endogenous oscillations over a period of approximately 24 hours, a phenomenon known as circadian rhythmicity. This rhythm typically reaches a nadir during sleep, however, different BP circadian rhythm phenotypes exist depending on the magnitude and direction of the nocturnal change. Analysis of these phenotypes has been shown to be an independent indicator for the onset of cardiovascular disease, the leading cause of non-communicable mortality and morbidity worldwide. However, currently the established technique for monitoring BP over 24 hours in the general population requires an inflatable cuff wrapped around the upper arm. This procedure is highly disruptive to sleep and daily life, and therefore rarely performed in primary care. Although commercial cuffless BP devices do exist, their accuracy has been questioned, and consequently, the clinical community do not recommend their use. In this thesis, I investigated techniques to measure BP in an ambulatory environment without an inflatable cuff using two signals commonly acquired by wearable sensors: the photoplethysmogram (PPG) and the electrocardiogram (ECG). Given the diverse mechanisms by which the autonomic nervous system regulates BP, I developed methodologies using data from multiple individuals with BP perturbed by various, diverse, mechanisms. To identify surrogate measures of BP derived from the PPG and ECG signals, I designed a clinical study in which significant BP changes were induced through a pharmacological intervention in thirty healthy volunteers. Using data from this study, I established that changes in the pulse arrival time (PAT, the time delay between fiducial points on the ECG and PPG waveforms) and morphological features of the PPG waveform could provide reliable cuffless indicators for changes in BP. Even at rest, however, these signals are confounded by factors such as the pre-ejection period (PEP) and signal measurement noise. Additionally, accurate absolute measurements of BP required calibration using a reference BP device. Subsequently, I conducted a circadian analysis of these surrogate measures of BP using a large cohort of 1,508 patients during the 24-hour period prior to their discharge from an intensive care unit. Through this circadian analysis I suggest that PAT and a subset of features from the PPG waveform exhibit a phenotypically modified circadian rhythm in synchronicity with that of BP. Additionally, I designed a novel ordinal classification algorithm, which utilised circadian features of these signals, in order to identify BP circadian rhythm profiles in a calibration-free manner. This method may provide a cost-effective initial assessment of BP phenotypes in the general population. Notably, estimating absolute BP values using PPG and ECG signals in the ICU resulted in clinically significant mean absolute errors of 9.26 (5.01) mmHg. Finally, I designed a clinical study to extend the work towards cuffless ambulatory BP estimation in a cohort of fifteen healthy volunteers. Hybrid calibration strategies (where model personalisation was handled by user demographics, commonly utilised by commercial cuffless devices) led to clinically significant errors when estimating absolute values of BP, mean absolute error = 9.62 (19.73) mmHg. For the majority of individuals, a more appropriate estimation of BP values was achieved through an individual calibration strategy whereby idiosyncratic models were trained on personalised data, mean absolute error = 5.45 (6.40) mmHg. However, for a handful of individuals, notable estimation errors (>10 mmHg) still persisted using this strategy largely as a result of motion artifacts, inherent intra- and inter-individual variability in PPG features, and inadequate training data. Overall, I suggest that while beat-by-beat measurements of BP can be obtained using PPG and ECG signals, their accuracy is significantly limited in an ambulatory environment. This limitation, combined with the impracticality of individual calibration (due to the low tolerance for ABPM), suggest that cuffless ambulatory blood pressure measurement using the PPG and ECG signals may be infeasible. Nevertheless, macro assessments of cardiovascular health, such as an individual's BP phenotype, may be comparatively more accurately predicted using these signals with the potential to be recorded without calibration. Through further research on the relationship between the circadian rhythms of BP and the PPG and ECG waveforms, it is promising that these signals may be able to assist in detecting deterioration in cardiovascular health in the general population

A Review of Deep Learning Methods for Photoplethysmography Data

Photoplethysmography (PPG) is a highly promising device due to its advantages in portability, user-friendly operation, and non-invasive capabilities to measure a wide range of physiological information. Recent advancements in deep learning have demonstrated remarkable outcomes by leveraging PPG signals for tasks related to personal health management and other multifaceted applications. In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed from three key perspectives: tasks, models, and data. We finally extracted 193 papers where different deep learning frameworks were used to process PPG signals. Based on the tasks addressed in these papers, we categorized them into two major groups: medical-related, and non-medical-related. The medical-related tasks were further divided into seven subgroups, including blood pressure analysis, cardiovascular monitoring and diagnosis, sleep health, mental health, respiratory monitoring and analysis, blood glucose analysis, as well as others. The non-medical-related tasks were divided into four subgroups, which encompass signal processing, biometric identification, electrocardiogram reconstruction, and human activity recognition. In conclusion, significant progress has been made in the field of using deep learning methods to process PPG data recently. This allows for a more thorough exploration and utilization of the information contained in PPG signals. However, challenges remain, such as limited quantity and quality of publicly available databases, a lack of effective validation in real-world scenarios, and concerns about the interpretability, scalability, and complexity of deep learning models. Moreover, there are still emerging research areas that require further investigation

Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

A novel method for the continual, cuff-less estimation of the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values based on signal complexity analysis of the photoplethysmogram (PPG) and the electrocardiogram (ECG) is reported. The proposed framework estimates the blood pressure (BP) values obtained from signals generated from 14 volunteers subjected to a series of exercise routines. Herein, the physiological signals were first pre-processed, followed by the extraction of complexity features from both the PPG and ECG. Subsequently the complexity features were used in regression models (artificial neural network (ANN), support vector machine (SVM) and LASSO) to predict the BP. The performance of the approach was evaluated by calculating the mean absolute error and the standard deviation of the predicted results and compared with the recommendations made by the British Hypertension Society (BHS) and Association for the Advancement of Medical Instrumentation. Complexity features from the ECG and PPG were investigated independently, along with the combined dataset. It was observed that the complexity features obtained from the combination of ECG and PPG signals resulted to an improved estimation accuracy for the BP. The most accurate DBP result of 5.15 ± 6.46 mmHg was obtained from ANN model, and SVM generated the most accurate prediction for the SBP which was estimated as 7.33 ± 9.53 mmHg. Results for DBP fall within recommended performance of the BHS but SBP is outside the range. Although initial results are promising, further improvements are required before the potential of this approach is fully realised

A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals

Systems and methods for physiological signal enhancement and biometric extraction using non-invasive optical sensors

A system and method for signal processing to remove unwanted noise components including: (i) wavelength-independent motion artifacts such as tissue, bone and skin effects, and (ii) wavelength-dependent motion artifact/noise components such as venous blood pulsation and movement due to various sources including muscle pump, respiratory pump and physical perturbation. Disclosed are methods, analytics, and their uses for reliable perfusion monitoring, arterial oxygen saturation monitoring, heart rate monitoring during daily activities and in hospital settings and for extraction of physiological parameters such as respiration information, hemodynamic parameters, venous capacity, and fluid responsiveness. The system and methods disclosed are extendable to include monitoring platforms for perfusion, hypoxia, arrhythmia detection, airway obstruction detection and sleep disorders including apnea.Board of Regents, University of Texas Syste

Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice