PPG pulse direction determination algorithm for PPG waveform inversion by wrist rotation

Exploration of the Conditions for Occurrence of Photoplethysmographic Signal Inversion above the Dorsalis Pedis Artery

Inversion of the photoplethysmographic (PPG) signal is a rarely reported case. This signal anomaly can have implications for PPG-based cardiovascular assessments. The conditions for PPG signal inversion in the vicinity of the dorsalis pedis (DPA) artery of the foot were investigated. Wireless multi-wavelength PPG sensing with skin-probe contact pressure and local skin temperature were studied at different sensor positions, and the occurrence of inversion (OOI) was investigated. Twelve healthy adult volunteers were studied over four LED wavelengths at three levels of contact pressure for 11 probe positions. A novel algorithm quantified the proportion of inverted samples with respect to the abovementioned variables. Our algorithm classifying inverted vs. non-inverted pulses achieved 98.3% accuracy. Ten of the participants had at least one inverted signal identified. The impact of interindividual variation on inversion prevalence was large, but different LEDs, relative position to the DPA and sensor contact pressure also affected OOI. Skin surface and room temperatures showed no impact on OOI. Lateral measurements showed 39.6% more inversion at maximum compared to minimum contact pressure. Mechanical capillary bed variations and arterial reflections during venous engorgement are considered viable explanations for our observations. These findings motivate an expanded study of the occurrence of PPG signal inversion.

Spatial dependency of the PPG morphology at right carotid common artery

PhotoPlethysmoGraphy (PPG) is ubiquitously employed in wearable devices for health monitoring. Photodiode signal inversion is observed in rare occasions, most of the time when the sensor is pressed against the skin. We report in this article such observations made at the right common carotid artery site. Indeed we have systematically observed a photodiode signal inversion when the PPG sensor is placed where the pulse is the best felt at the carotid. In addition to be inverted, the pulse is steeper during the systolic phase. Such inversion has implications in terms of pulse arrival time (PAT) measurements In our experiments, this causes a difference of 20 ms in the carotid PAT when measured at the absolute maximum slope. The mechanical and optical properties of tissues must be better accounted to explain the PPG signal morphology. Clinical Relevance- Understanding the role of mechanical tissue properties seems relevant in order to obtain more reproducible results in PPG signal analysis.

Optimal Preprocessing of Raw Signals from Reflective Mode Photoplethysmography in Wearable Devices

The optical measurement principle photoplethysmography has emerged in today's wearable devices as the standard to monitor the wearer's heart rate in everyday life. This cost-effective and easy-to-integrate technique has transformed from the original transmission mode pulse oximetry for clinical settings to the reflective mode of modern ambulatory, wrist-worn devices. Numerous proposed algorithms aim at the efficient heart rate measurement and accurate detection of the consecutive pulses for the derivation of secondary features from the heart rate variability. Most, however, have been evaluated either on own, closed recordings or on public datasets that often stem from clinical pulse oximeters in transmission instead of wearables' reflective mode. Signals tend furthermore to be preprocessed with filters, which are rarely documented and unintentionally fitted to the available and applied signals. We investigate the influence of preprocessing on the peak positions and present the benchmark of two cutting-edge pulse detection algorithms on actual raw measurements from reflective mode photoplethysmography. Based on 21806 pulse labels, our evaluation shows that the most suitable but still universal filter passband is located at 0.5 to 15.0Hz since it preserves the required harmonics to shape the peak positions.

A Tactile-Pattern-Integrated Sensing Window for More Consistent Photoplethysmography (PPG) Measurements

We have demonstrated a tactile-pattern-integrated sensing window for more consistent photoplethysmogram (PPG) measurements. The pattern is composed of two tiny bumps that measure 500μm in diameter and 300μm in height and allow users to position their finger pulps more consistently on the sensing window over different measurement occasions, simply by following their tactile sensation. We experimentally compared the tactile pattern window to a flat window (without any bumps) for 5 test subjects and found that the sensing window with the tactile pattern significantly helped users obtain more consistent PPG signals than the flat window (p < 0.01).The use of PPG sensors in mobile phones and wearable watches have been limited to the measurements of heart rates and blood oxygen saturation in spite of widely-spread efforts to expand their applications. This is due to the fluctuations observed between measurements which largely originate from inconsistent placement of fingers on the sensing windows. The integrated tactile pattern could provide consistent and accurate measurements and lead to more successful commercialization of diverse PPG-based mobile healthcare services.

Pulse sharpness as a quantitative index of vascular aging

The aim of this study was to develop a robust algorithm to quantify pulse sharpness that can complement the limitations of radial augmentation index (rAIx) and explore the role of this quantitative sharpness index in reflecting vascular aging or arterial stiffness. The pulse sharpness index (PSI) was developed by combining the end point angle and virtual height, and 528 radial pulses were analyzed. The PSI could be uniformly applied to various waveform morphologies, even those with no or vague tidal waves, unlike the rAIx. Significant sex differences were identified in the rAIx and PSI (P < 0.01 for both), and significant age-dependent decreases in the PSI were observed (P < 0.01). In addition, the PSI and age were correlated (r = - 0.550) at least as strong as the rAIx and age (r = 0.532), and the PSI had a significant negative correlation with arterial stiffness (r = - 0.700). Furthermore, the multiple linear regression model for arterial stiffness using the PSI, age, sex and heart rate showed the excellent performance (cross-validated R2 = 0.701), and the PSI was found to have the greatest influence on arterial stiffness. This study confirmed that the PSI could be a quantitative index of vascular aging and has potential for use in inferring arterial stiffness with an advantage over the rAIx.

Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology

Background

There has been a recent increased interest in monitoring health using wearable sensor technologies; however, few have focused on breathing. The ability to monitor breathing metrics may have indications both for general health as well as respiratory conditions such as asthma, where long-term monitoring of lung function has shown promising utility.

Objective

In this paper, we explore a long short-term memory (LSTM) architecture and predict measures of interbreath intervals, respiratory rate, and the inspiration-expiration ratio from a photoplethysmogram signal. This serves as a proof-of-concept study of the applicability of a machine learning architecture to the derivation of respiratory metrics.

Methods

A pulse oximeter was mounted to the left index finger of 9 healthy subjects who breathed at controlled respiratory rates. A respiratory band was used to collect a reference signal as a comparison.

Results

Over a 40-second window, the LSTM model predicted a respiratory waveform through which breathing metrics could be derived with a bias value and 95% CI. Metrics included inspiration time (–0.16 seconds, –1.64 to 1.31 seconds), expiration time (0.09 seconds, –1.35 to 1.53 seconds), respiratory rate (0.12 breaths per minute, –2.13 to 2.37 breaths per minute), interbreath intervals (–0.07 seconds, –1.75 to 1.61 seconds), and the inspiration-expiration ratio (0.09, –0.66 to 0.84).

Conclusions

A trained LSTM model shows acceptable accuracy for deriving breathing metrics and could be useful for long-term breathing monitoring in health. Its utility in respiratory disease (eg, asthma) warrants further investigation.