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Prediction of vascular ageing based on smartphone acquired PPG signals

By Lorenzo Dall'Olio, Nico Curti, Daniel Remondini, Yosef Safi Harb, F.W. Asselbergs, Gastone Castellani, Hae-Won Uh

Posted 28 May 2020
bioRxiv DOI: 10.1101/2020.05.26.116186 (published DOI: 10.1038/s41598-020-76816-6)

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular ageing is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular ageing (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. ### Competing Interest Statement Yosef Safi Harb is a shareholder at Happitech. The other authors declare that they have no competing interests.

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