Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

ABSTRACT:

Background

Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults.

Methods

Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood p

SUBMITTER: Yilmaz G 

PROVIDER: S-EPMC10537552 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature