Photoplethysmographic Prediction of the Ankle-Brachial Pressure Index through a Machine Learning Approach

Abstract

Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

“…Moreover, Phillips et al applied PPG sensors to non-invasively evaluate hemoglobin concentration [ 8 ]. Meanwhile, Perpetuini et al supervised a general linear model-based PPG to evaluate the ankle–brachial index, which was initially measured using a commercial instrument as the gold standard [ 9 ]. In a recent study, entropy-based PPG evaluations have been successfully applied to distinguish between healthy and diabetic patients [ 10 ].…”

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

“…The coupling between electrical and hemodynamic brain activity was evaluated convolving the EEG signal with the canonical hemodynamic response. Finally, a cross-validated multivariate data-driven (i.e., Machine Learning) analysis based on a General Linear Model [ 41 , 42 , 43 ] employing all the evaluated complexity metrics as input was performed to classify AD and HC. Notably, the multivariate approach proposed provided a single dependent variable (i.e., label of the disease) and multiple independent features (i.e., complexity metrics).…”

Prediction of state anxiety by machine learning applied to photoplethysmography data

“…1A ) equipped with seven optical probes. As previously described in Perpetuini et al (2020) , the optical probes were developed by STMicroelectronics (Catania, Italy) and they were composed by a light source consisting in a Light Emitting Diode (LED) emitting a wavelength range centered at 940 nm (SMC940 LED, Roithner Laser Technik, Vienna, Austria) coupled with a detector composed of Silicon PhotoMultiplier (SiPM) chip ( Vinciguerra et al, 2017 ). Light source (LED) and detector (SiPM) were located at an inter-optical distance of 4 cm.…”

“…The signal pre-processing was performed in accordance with Perpetuini et al (2020) . Specifically, raw PPG signals were converted into optical densities (ODs) ( Chiarelli et al, 2019b ).…”

“…For instance, PPG is commonly used to monitor oxygen saturation on fingers or earlobes using multi-wavelengths recordings. Moreover, single-wavelength PPG can also evaluate arterial stiffness ( Chiarelli et al, 2019a ; Perpetuini et al, 2020 ) assessing PPG shape and speed of propagation of the pulse wave (e.g., Pulse Wave Velocity, PWV) at multiple body locations ( Nandini & Pandey, 2018 ). Furthermore, PPG signal is associated to intravascular pressure and several models have been proposed to predict blood pressure from PPG ( Chandrasekaran et al, 2012 ; Ding et al, 2015 ; Lazazzera, Belhaj & Carrault, 2019 ).…”