Development of a Signal Processing Library for Extraction of SpO2, HR, HRV, and RR from Photoplethysmographic Waveforms

Non-invasive remote physiological monitoring of soldiers on the battlefield has the potential to provide fast, accurate status assessments that are key to improving the survivability of critical injuries. The development of WPI’s wearable wireless pulse oximeter, designed for field-based applications, has allowed for the optimization of important hardware features such as physical size and power management. However, software-based digital signal processing (DSP) methods are still required to perform physiological assessments. This research evaluated DSP methods that were capable of providing arterial oxygen saturation (SpO2), heart rate (HR), heart rate variability (HRV), and respiration rate (RR) measurements derived from data acquired using a single optical sensor. In vivo experiments were conducted to evaluate the accuracies of the processing methods across ranges of physiological conditions. Of the algorithms assessed, 13 SpO2 methods, 1 HR method, 6 HRV indices, and 4 RR methods were identified that provided clinically acceptable measurement accuracies and could potentially be employed in a wearable pulse oximeter

Heart Rate Estimation During Physical Exercise Using Wrist-Type Ppg Sensors

Accurate heart rate monitoring during intense physical exercise is a challenging problem due to the high levels of motion artifacts (MA) in photoplethysmography (PPG) sensors. PPG is a non-invasive optical sensor that is being used in wearable devices to measure blood flow changes using the property of light reflection and absorption, allowing the extraction of vital signals such as the heart rate (HR). However, the sensor is susceptible to MA which increases during physical activity. This occurs since the frequency range of movement and HR overlaps, difficulting correct HR estimation. For this reason, MA removal has remained an active topic under research. Several approaches have been developed in the recent past and among these, a Kalman filter (KF) based approach showed promising results for an accurate estimation and tracking using PPG sensors. However, this previous tracker was demonstrated for a particular dataset, with manually tuned parameters. Moreover, such trackers do not account for the correct method for fusing data. Such a custom approach might not perform accurately in practical scenarios, where the amount of MA and the heart rate variability (HRV) depend on numerous, unpredictable factors. Thus, an approach to automatically tune the KF based on the Expectation-Maximization (EM) algorithm, with a measurement fusion approach is developed. The applicability of such a method is demonstrated using an open-source PPG database, as well as a developed synthetic generation tool that models PPG and accelerometer (ACC) signals during predetermined physical activities

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

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

Optimization of multi-wavelength Photoplethysmographic for wearable heart rate acquisition

Photoplethysmographic is an optical measure technique for heart rate monitoring on the surface of the skin. PPG based wearable heart rate monitor has become popular in consumer targeted market. This thesis work is based on the PulseOn product development and the final implementation will be integrated into the PulseOn OHRM sensor product. Choice of the wavelength of PPG is a trade-off between power consumption and accuracy considering the activity type, skin color and skin perfusion. The subject of this thesis is implementing a channel selection algorithm, which is green and IR channel, on a commercially available PulseOn wrist band to optimize the power consumption and accuracy of the measurement. The channel selection algorithm is first implemented and evaluated in Matlab simulation and then implemented in C code. Performance of the channel selection algorithm on the device is evaluated considering various factors, including skin color, tightness of the wristband. The results show that channel selection algorithm can not only reduce the power consumption but also help to handle the measurement on different measurement conditions

Noncontact blood perfusion mapping in clinical applications

© 2016 SPIE.Non-contact imaging photoplethysmography (iPPG) to detect pulsatile blood microcirculation in tissue has been selected as a successor to low spatial resolution and slow scanning blood perfusion techniques currently employed by clinicians. The proposed iPPG system employs a novel illumination source constructed of multiple high power LEDs with narrow spectral emission, which are temporally modulated and synchronised with a high performance sCMOS sensor. To ensure spectrum stability and prevent thermal wavelength drift due to junction temperature variations, each LED features a custom-designed thermal management system to effectively dissipate generated heat and auto-adjust current flow. The use of a multi-wavelength approach has resulted in simultaneous microvascular perfusion monitoring at various tissue depths, which is an added benefit for specific clinical applications. A synchronous detection algorithm to extract weak photoplethysmographic pulse-waveforms demonstrated robustness and high efficiency when applied to even small regions of 5 mm2. The experimental results showed evidences that the proposed system could achieve noticeable accuracy in blood perfusion monitoring by creating complex amplitude and phase maps for the tissue under examination

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Conduit Artery Photoplethysmography and its Applications in the Assessment of Hemodynamic Condition

Elektroniskā versija nesatur pielikumusPromocijas darbā ir izstrādāta maģistrālo artēriju fotopletizmogrāfijas (APPG) metode hemodinamisko parametru novērtējumam. Pretstatot referentām metodēm, demonstrēta iespēja iegūt arteriālo elasticitāti raksturojošus parametrus, izmantojot APPG signāla formas analīzi (atvasinājuma un signāla formas aproksimācijas parametri) un ar APPG iegūtu pulsa izplatīšanās ātrumu unilaterālā gultnē. Izstrādāta APPG reģistrācijas standartizācija, mērījuma laikā nodrošinot optimālo sensora piespiedienu. Šis paņēmiens validēts ārējās ietekmes (sensora piespiediens) un hemodinamisko stāvokļu (perifērā vaskulārā pretestība) izmaiņās femorālā APPG signālā, identificējot būtiskākos faktorus APPG pielietojumos. Veikta APPG validācija asinsrites fizioloģijas un preklīniskā pētījumā demonstrējot APPG potenciālu pētniecībā un diagnostikā. Izstrādāts pulsa formas parametrizācijas paņēmiens, saistot fizioloģiskās un aproksimācijas modeļa komponentes. Atslēgas vārdi: maģistrālā artērija, fotopletizmogrāfija, arteriālā elasticitāte, metodes standartizācija, pulsa formas kvantifikācija, vazomocija, sepseThe doctoral thesis features the development of a conduit artery photoplethysmography technique (APPG) for the evaluation of hemodynamic parameters. Contrasting referent methods, the work demonstrates the possibility to receive parameters characterizing the arterial stiffness by means of APPG waveform analysis (derivation and waveform approximation parameters) and APPG obtained pulse wave velocity in a unilateral vascular bed. In this work APPG standardization technique was developed providing optimal probe contact pressure conditions. It was validated by altering the external factors (probe contact pressure) and hemodynamic conditions (peripheral vascular resistance) on the femoral APPG waveform identifying the key factors in APPG applications. The APPG validation in blood circulation physiology and a pre-clinical trial was performed demonstrating APPG potential in the extension of applications. An arterial waveform parameterization was developed relating the physiological wave to approximation model components. Keywords: conduit artery, photoplethysmography, arterial stiffness, method standardization, waveform parametrization, vasomotion, sepsi

Photoplethysmography-Based Biomedical Signal Processing

In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal. Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods. The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration. All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets