Classifying nocturnal blood pressure patterns using photoplethysmogram features

Circadian rhythms in blood pressure (BP) may in some cases be indicative of an increased risk of adverse cardiovascular events. However, current methods for assessing these rhythms can be disruptive to sleep, work, and daily activities. Features of the photoplethysmogram (PPG), which can be non-invasively and unobtrusively recorded, have been suggested as surrogate measures of BP. This work investigates the presence of a circadian rhythm in these features and evaluates their potential to classify nocturnal BP patterns. 742 patients who were discharged home from the ICU were selected from the MIMIC-III database. Our results show that a number of PPG features exhibit a clear and observable circadian rhythm. Of the 19 features evaluated, the circadian rhythms of 5 features outperformed heart rate (HR) in terms of correlation with the circadian rhythm of SBP ( ). We also present evidence that a metric combining the PPG features significantly improves BP phenotype classification accuracy. Clinical Relevance-This work suggests that a combined metric of PPG features may be able to accurately assess an individual's circadian rhythm of BP.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:2022

Enthalten in:

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2022(2022) vom: 09. Juli, Seite 3401-3404

Sprache:

Englisch

Beteiligte Personen:

Finnegan, Eoin [VerfasserIn]
Davidson, Shaun [VerfasserIn]
Harford, Mirae [VerfasserIn]
Jorge, Joao [VerfasserIn]
Villarroel, Mauricio [VerfasserIn]
Tarassenko, Lionel [VerfasserIn]

Links:

Volltext

Themen:

Journal Article

Anmerkungen:

Date Completed 13.09.2022

Date Revised 20.10.2022

published: Print

Citation Status MEDLINE

doi:

10.1109/EMBC48229.2022.9871099

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM346033624