Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?

• Main Authors: Gloria Martínez, Newton Howard, Derek Abbott, Kenneth Lim, Rabab Ward, Mohamed Elgendi
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Journal of Clinical Medicine
• Subjects: pulse morphology; pulse oximeter; blood pressure monitoring; pulse arrival time; global health; digital medicine; wearable devices
• Online Access: http://www.mdpi.com/2077-0383/7/10/316

Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.