Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device.

ABSTRACT:

Introduction

Though wrist-worn photoplethysmography (PPG) sensors play an important role in long-term and continuous heart rhythm monitoring, signals measured at the wrist are contaminated by more intense motion artifacts compared to other body locations. Machine learning (ML)-based algorithms can improve long-term pulse rate (PR) tracking but are associated with more stringent regulatory requirements when intended for clinical use. This study aimed to evaluate the accuracy of a digital health technology using wrist-worn PPG sensors and an ML-based algorithm to measure PR continuously.

Methods

Volunteers were enrolled in three independent clinical trials and concurrently monitored with the investigational device and FDA-cleared electrocardiography (ECG) devices during superv

SUBMITTER: Chen W 

PROVIDER: S-EPMC11637493 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature