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ResCSRF: Algorithm to Automatically Extract Cheyne-Stokes Respiration Features From Respiratory Signals.
IEEE Transactions on Bio-Medical Engineering
|June 11, 2017
Summary
An automated algorithm, ResCSRF, accurately scores Cheyne-Stokes respiration (CSR) features from respiratory signals, aiding in cardiac dysfunction monitoring.
Area of Science:
- Cardiology
- Respiratory Medicine
- Biomedical Engineering
Background:
- Cheyne-Stokes respiration (CSR) is linked to cardiac dysfunction.
- Manual scoring of CSR features is time-consuming and labor-intensive.
Purpose of the Study:
- To develop an automated algorithm (ResCSRF) for extracting CSR-related features from respiratory signals.
- To assess the performance of ResCSRF compared to manual expert scoring.
Main Methods:
- ResCSRF processes nasal flow, thorax, abdomen, and oxygen saturation signals.
- The algorithm detects CSR cycles and calculates features like cycle length and circulation time.
- Developed and tested on 49 chronic heart failure patients using overnight polygraphy.
Main Results:
- ResCSRF demonstrated high correlation with expert scoring for CSR features.
- Mean differences were minimal (less than 3s) with low reproducibility coefficients (less than 7s).
- Accuracy in CSR percentage per recording was comparable to manual scoring.
Conclusions:
- ResCSRF effectively automates the scoring of CSR-related features.
- The algorithm shows potential for integration into remote monitoring systems for cardiac function.
- Automated scoring can facilitate regular patient monitoring and management.
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