Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

Abstract

Pulse transit time (PTT) provides a cuffless method to measure and predict blood pressure, which is essential in long term cardiac activity monitoring. Photoplethysmography (PPG) sensors provide a low-cost and wearable approach to obtain PTT measurements. The current approach to calculating PTT relies on quasi-periodic pulse event extractions based on PPG local signal characteristics. However, due to inherent noise in PPG, especially at uncontrolled settings, this approach leads to significant errors and even missing potential pulse events. In this paper, we propose a novel approach where global features (all samples) of the time-series data are used to develop a machine learning model to extract local pulse events. Specifically, we contribute 1) a new noise resilient machine learning model to extract events from PPG and 2) results from a study showing accuracy over state of the art (e.g. HeartPy) and 3) we show that MLPTT outperforms HeartPy peak detection, especially for noisy photoplethysmography data.

Increasing accuracy of pulse arrival time estimation in low frequency recordings

“…The method has been developed and tested on the pulse transit time PPG Dataset (Mehrgardt et al 2022), publicly available on PhysioNet (Goldberger et al 2000). The dataset consists of data gathered from 22 healthy participants (of which 6 female) in an age range between 20 and 53 (with a mean of 28.52 years old), who performed three activities (sitting, walking, and running) in random order, for a total of 1 h per participant.…”