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Detection of congestive heart failure from RR intervals during long-term electrocardiographic recordings
Teemu Pukkila1, Matti Molkkari1, Jussi Hernesniemi2,3,4
1Computational Physics Laboratory, Tampere University, Tampere, Finland.
Insights
This study shows dynamical detrended fluctuation analysis (DDFA) can detect congestive heart failure (CHF) and atrial fibrillation (AF) using heart rate data. The method offers a cost-effective approach for early cardiac screening.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Timely detection of cardiovascular diseases is critical for effective management.
- Computational analysis of RR interval (RRI) sequences offers a cost-effective method for early cardiac screening using consumer heart rate devices.
Purpose of the Study:
- To demonstrate the detection of congestive heart failure (CHF) from RRIs.
- To discriminate CHF from healthy controls and patients with atrial fibrillation (AF).
- To examine the consistency of detection concerning CHF severity and AF episode frequency.
Main Methods:
- Analysis of RRIs from long-term electrocardiographic (ECG) recordings.
- Application of detrended fluctuation analysis (DFA) and dynamical detrended fluctuation analysis (DDFA) to evaluate RRI correlations.
- Classification using (D)DFA scaling exponents with the XGboost ensemble learning technique to distinguish between CHF, AF, and healthy controls.
Main Results:
- Distinct RRI characteristics were identified for CHF and AF patients, aiding disease detection.
- The DDFA-based pipeline achieved 90% sensitivity and 92% specificity in detecting CHF/AF from healthy controls.
- The 3-class classification correctly identified 78% of AF cases, 78% of CHF cases, and 91% of healthy cases, with consistent results across varying disease severity and AF episode frequency.
Conclusions:
- High confidence in detecting CHF and AF was achieved using DDFA, demonstrating excellent classification accuracy, particularly in multiclass scenarios.
- This noninvasive, cost-efficient RRI analysis approach shows significant potential for the early detection of CHF and AF.
Background:
Timely detection is crucial for managing cardiovascular diseases. Recently developed computational tools to analyze RR interval (RRI) sequences offer cost-effective means for early cardiac screening and monitoring with consumer-grade heart rate devices.
Objective:
The purpose of this study was to demonstrate detection of congestive heart failure (CHF) from RRIs by discriminating CHF from both healthy controls and patients with present atrial fibrillation (AF). We also examined the detection's consistency regarding CHF severity and AF episode frequency.
Methods:
We analyzed RRIs extracted from several datasets of long-term electrocardiographic (ECG) recordings. We use detrended fluctuation analysis (DFA) to evaluate the correlations of RRI, that is, how changes in the RRIs affect changes at another time. Furthermore, we utilized dynamical detrended fluctuation analysis (DDFA), which provides further insights into how the correlations change over time and different time scales. The resulting (D)DFA scaling exponents are used as features in classification, distinguishing CHF, AF, and healthy controls using the XGboost ensemble learning technique.
Results:
Our (D)DFA computations revealed distinct RRI characteristics for CHF and AF patients during long-term ECG recordings, aiding disease detection. The DDFA-based classification pipeline detects CHF/AF from healthy controls with 90% sensitivity and 92% specificity. The 3-class classification algorithm correctly detects 78% of AF cases, 78% of CHF cases, and 91% of healthy cases. The DDFA results show consistency regarding CHF severity and AF episode frequency.
Conclusion:
We achieved high confidence in detecting CHF, with DDFA showing excellent classification accuracy, especially in multiclass tasks. This approach highlights the potential of noninvasive, cost-efficient RRI analysis for early detection of CHF and AF.
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