Optimal timing in screening patients with congestive heart failure and healthy subjects during circadian observation

Area of Science:

• Cardiology
• Biomedical Engineering
• Data Science

Background:

• Congestive heart failure (CHF) presents a significant healthcare challenge.
• Accurate and rapid screening of CHF patients is crucial for timely intervention.
• Circadian rhythm influences physiological parameters, including heart rate variability (HRV).

Purpose of the Study:

• To determine the optimal timing for screening congestive heart failure (CHF) patients using heart rate variability (HRV) during circadian observation.
• To evaluate the efficacy of the α1 parameter in detrended fluctuation analysis for distinguishing CHF from normal sinus rhythm (NSR).
• To assess the performance of a support vector machine (SVM) for CHF classification based on HRV parameters.

Main Methods:

• Utilized interbeat interval data from 29 CHF patients and 54 healthy subjects.
• Applied detrended fluctuation analysis (DFA) to calculate the α1 parameter of HRV.
• Employed the Fano factor to identify optimal screening times.
• Used a support vector machine (SVM) with leave-one-out cross-validation for classification.
• Analyzed ECG recordings of varying lengths (2–120 min).

Main Results:

• The α1 parameter of HRV effectively differentiates CHF patients from NSR subjects (Kruskal-Wallis test).
• The optimal screening period identified was between 7 PM and 9 PM.
• This finding remained robust across different ECG recording durations.
• SVM classification achieved superior performance for CHF detection during the 7 PM–9 PM window.

Conclusions:

• The 7 PM to 9 PM circadian period offers optimal timing for non-invasive screening of CHF patients.
• The α1 parameter derived from HRV analysis is a reliable indicator for CHF screening.
• These findings support the clinical application of HRV analysis for efficient CHF monitoring.