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

Assessing mental stress from the photoplethysmogram: a numerical study.

OBJECTIVE: Mental stress is detrimental to cardiovascular health, being a risk factor for coronary heart disease and a trigger for cardiac events. However, it is not currently routinely assessed. The aim of this study was to identify features of the photoplethysmogram (PPG) pulse wave which are indicative of mental stress. APPROACH: A numerical model of pulse wave propagation was used to simulate blood pressure signals, from which simulated PPG pulse waves were estimated using a transfer function. Pulse waves were simulated at six levels of stress by changing the model input parameters both simultaneously and individually, in accordance with haemodynamic changes associated with stress. Thirty-two feature measurements were extracted from pulse waves at three measurement sites: the brachial, radial and temporal arteries. Features which changed significantly with stress were identified using the Mann-Kendall monotonic trend test. MAIN RESULTS: Seventeen features exhibited significant trends with stress in measurements from at least one site. Three features showed significant trends at all three sites: the time from pulse onset to peak, the time from the dicrotic notch to pulse end, and the pulse rate. More features showed significant trends at the radial artery (15) than the brachial (8) or temporal (7) arteries. Most features were influenced by multiple input parameters. SIGNIFICANCE: The features identified in this study could be used to monitor stress in healthcare and consumer devices. Measurements at the radial artery may provide superior performance than the brachial or temporal arteries. In vivo studies are required to confirm these observations

Acceleration plethysmogram based biometric identification

This paper presents the feasibility study of Acceleration Plethysmogram (APG) based biometric identification system. APG signals are obtained from the second derivative of the Photoplethysmogram (PPG) signal. It has been reported from previous literature that APG signals contain more information as compared to the PPG signal. Thus, in this paper, the robustness and reliability of APG signal as a biometric recognition mechanism will be proven. APG signals of 10 subjects were acquired from the Multiparameter Intelligent Monitoring in Intensive Care II Waveform Database (MIMIC2WDB) which contains PPG signals with a sampling frequency of 125 Hz. The signals were later converted into an APG waveform. Then, discriminating features are extracted from the APG morphology. Finally, these APG samples were classified using commonly known classification techniques to identify individuals. Based on the experimentation results, APG signal when using Bayes Network gives an identification rate of 97.5 percentage as compared to PPG signal of 55 percentage for the same waveform. This outcome suggests the feasibility and robustness of APG signals as a biometric modality as compared to PPG signals

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals

Recent clinical studies show that the contour of the photoplethysmogram (PPG) wave contains valuable information for characterizing cardiovascular activity. However, analyzing the PPG wave contour is difficult; therefore, researchers have applied first or higher order derivatives to emphasize and conveniently quantify subtle changes in the filtered PPG contour. Our hypothesis is that analyzing the whole PPG recording rather than each PPG wave contour or on a beat-by-beat basis can detect heat-stressed subjects and that, consequently, we will be able to investigate the impact of global warming on human health. Here, we explore the most suitable derivative order for heat stress assessment based on the energy and entropy of the whole PPG recording. The results of our study indicate that the use of the entropy of the seventh derivative of the filtered PPG signal shows promising results in detecting heat stress using 20-second recordings, with an overall accuracy of 71.6%. Moreover, the combination of the entropy of the seventh derivative of the filtered PPG signal with the root mean square of successive differences, or RMSSD (a traditional heart rate variability index of heat stress), improved the detection of heat stress to 88.9% accuracy

A pilot study: can heart rate variability (HRV) be determined using short-term photoplethysmograms?

To date, there have been no studies that investigate the independent use of the photoplethysmogram (PPG) signal to determine heart rate variability (HRV). However, researchers have demonstrated that PPG signals offer an alternative way of measuring HRV when electrocardiogram (ECG) and PPG signals are collected simultaneously. Based on these findings, we take the use of PPGs to the next step and investigate a different approach to show the potential independent use of short 20-second PPG signals collected from healthy subjects after exercise in a hot environment to measure HRV. Our hypothesis is that if the PPG--HRV indices are negatively correlated with age, then short PPG signals are appropriate measurements for extracting HRV parameters. The PPGs of 27 healthy male volunteers at rest and after exercise were used to determine the HRV indices: standard deviation of heartbeat interval (SDNN) and the root-mean square of the difference of successive heartbeats (RMSSD). The results indicate that the use of the $aa$ interval, derived from the acceleration of PPG signals, is promising in determining the HRV statistical indices SDNN and RMSSD over 20-second PPG recordings. Moreover, the post-exercise SDNN index shows a negative correlation with age. There tends to be a decrease of the PPG--SDNN index with increasing age, whether at rest or after exercise. This new outcome validates the negative relationship between HRV in general with age, and consequently provides another evidence that short PPG signals have the potential to be used in heart rate analysis without the need to measure lengthy sequences of either ECG or PPG signals.Mohamed Elgendi, Ian Norton, Matt Brearley, Socrates Dokos, Derek Abbott, Dale Schuurman

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Due to the high demands of tiny, compact, lightweight, and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often severely corrupted under ambulatory and exercise recording conditions, leading to frequent false alarms. In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding techniques for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without the use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR, RR estimation, and SQA results are compared with the existing methods and results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. The proposed unified qualityaware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Arduino Due computing platformbased implementation demonstrates the real-time feasibility of the proposed unified quality-aware PRRR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.publishedVersio

Detection of a and b waves in the acceleration photoplethysmogram

Background: Analyzing acceleration photoplethysmogram (APG) signals measured after exercise is challenging. In this paper, a novel algorithm that can detect a waves and consequently b waves under these conditions is proposed. Accurate a and b wave detection is an important first step for the assessment of arterial stiffness and other cardiovascular parameters. Methods: Nine algorithms based on fixed thresholding are compared, and a new algorithm is introduced to improve the detection rate using a testing set of heat stressed APG signals containing a total of 1,540 heart beats. Results: The new a detection algorithm demonstrates the highest overall detection accuracy—99.78% sensitivity, 100% positive predictivity—over signals that suffer from 1) non-stationary effects, 2) irregular heartbeats, and 3) low amplitude waves. In addition, the proposed b detection algorithm achieved an overall sensitivity of 99.78% and a positive predictivity of 99.95%. Conclusions: The proposed algorithm presents an advantage for real-time applications by avoiding human intervention in threshold determination.Mohamed Elgendi, Ian Norton, Matt Brearley, Derek Abbott, and Dale Schuurman

pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis

Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.orgComment: The manuscript was submitted to "Physiological Measurement" on September 5, 202