Nature Scientific Reports – Performance of an Automated Photoplethysmography-Based Algorithm

Nature Scientific Reports – Performance of an Automated Photoplethysmography-Based Algorithm

5 Nov, 2020

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

Introduction

Photoplethysmography (PPG) offers a non-invasive optical measurement method and can be used for heart rate (HR) monitoring purposes. For example, by using the white light-emitting diode as light source and the phone camera as photo-detector positioned on the index fingertip, a smartphone could be used to measure the volumetric variations of blood circulation without any additional devices. Smartphone ownership continues to grow rapidly, and mobile phone apps are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). The benefits and harms of using mobile apps to improve cardiovascular health or screening for specific diseases have been evaluated and discussed; while HR measured by smartphone apps performing PPG agrees with a validated method such as electrocardiogram (ECG) in resting sinus rhythm, the US Preventive Services Task Force (USPSTF) found that the current evidence is insufficient to assess the balance of benefits and harms of screening for AF with ECG.

Our motivating crowd-sourced data comes from the Heart for Heart (H4H) initiative, promoted by the Arrhythmia Alliance, the Atrial Fibrillation Association, Happitech and other partners. The aim of H4H is to gather millions of cardiac measurements and to increase the pace of progress on AF diagnostic technology.

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