Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment.
Mohamed ElgendiFridolin HauggRichard Ribon FletcherJohn AllenHangsik ShinAymen AlianCarlo MenonPublished in: Communications medicine (2024)
Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy. Here, we review seven widely used measures to assess PPG-based algorithm performance and recommend implementing standardized error evaluation steps in their development. This standardization can reduce bias and improve the reliability and accuracy of PPG-based BP estimation, leading to better health outcomes for patients managing hypertension.
Keyphrases
- blood pressure
- heart rate
- machine learning
- hypertensive patients
- deep learning
- end stage renal disease
- chronic kidney disease
- ejection fraction
- newly diagnosed
- prognostic factors
- high resolution
- blood glucose
- metabolic syndrome
- drinking water
- risk assessment
- quality improvement
- patient reported outcomes
- adipose tissue
- clinical practice
- insulin resistance
- weight loss
- patient reported