ECG and PPG-Based Hypertension Screening Under Non-Hypertensive Blood Pressure Recordings

Blood pressure (BP) fluctuates throughout the day, mainly due to circadian oscillations as well as a response to physical and mental stimuli. This study aims at investigating whether machine learning (ML) classifiers can detect hypertension pathology regardless of absolute BP values. The goal is to identify HTS patients from non-HTS recordings and NTS subjects from non-NTS recordings using photoplethysmography (PPG) and electrocardiography (ECG). 803 simultaneous PPG, ECG and invasive BP recordings from 51 subjects were analyzed. 668 were coherent BP segments, with high BP for HTS patients and normal BP for NTS subjects, and 135 were incoherent segments, with normal BP for HTS patients and high BP for NTS subjects. PPG and BP relationship was evaluated with discriminant features and classification models were employed to classify incoherent segments. Using the discriminant features of coherent segments for training and the set of incoherent segments for validation, K-nearest neighbors provided the best outcomes, with F1-score of 88.30%. Combining PPG and ECG recordings with ML-based methodologies would be of high interest for hypertension screening, so that HTS and NTS subjects could be properly discerned even in the case of incoherent or altered BP values. This method could be used as a support for clinical decision-making when diagnosing hypertension.