Key result
Novel cloud-based PPG system discriminates CHF from healthy controls with ~99% sensitivity.
Why the study?
Many individuals lack symptoms before sudden cardiac death, limiting pre-event risk identification and driving the need for timely, accurate prediction using new electronic technologies.
Does a novel e-health cloud-based system using natural time entropy variability analysis of PPG signals accurately discriminate chronic heart failure patients from healthy controls?
Cross-Sectional (n=203)
No
Does a novel e-health cloud-based system using natural time entropy variability analysis of PPG signals accurately discriminate chronic heart failure patients from healthy controls?
A novel cloud-based PPG system using natural time analysis can accurately distinguish patients with chronic heart failure from healthy controls using just 10 minutes of data, offering a potential non-invasive tool for sudden cardiac death risk stratification.
Answers are grounded in this paper and cite their sources.
May support non-invasive CHF screening via e-health; leaves open prospective validation for SCD risk stratification.
Sudden cardiac death (SCD) is one of the leading causes of death worldwide. Many individuals have no cardiovascular symptoms before the SCD event. As a result, the ability to identify the risk before such an event is extremely limited. Timely and accurate prediction of SCD using new electronic technologies is greatly needed. In this work, a new innovative e-health cloud-based system is presented that allows a stratification of SCD risk based on the method of natural time entropy variability analysis. This innovative, non-invasive system can be used easily in any setting. The e-health cloud-based system was evaluated using data from a total of 203 individuals, patients with chronic heart failure (CHF) who are at high risk of SCD and age-matched healthy controls. Statistical analysis was performed in two-time windows of different duration; the first-time window had a duration of 20 min, while the second was 10 min. Employing modern methods of machine learning, classifiers for the discrimination of CHF patients from the healthy controls were obtained for the first as well as the second (half-time) window. The results indicated a very good separation between the two groups, even from samples taken in a 10-min time window. Larger studies are needed to further validate this novel e-health cloud-based system before its use in everyday clinical practice.
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Tatsis et al. (2023) conducted a cross-sectional in Chronic heart failure (n=203). E-health cloud-based system using photoplethysmography and natural time analysis vs. Healthy controls was evaluated on Discrimination of chronic heart failure patients from healthy controls (sensitivity). A novel e-health cloud-based system using photoplethysmography and natural time analysis successfully discriminated chronic heart failure patients from healthy controls with 99.4% sensitivity for CHF and 93.8% sensitivity for healthy individuals.
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