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Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal
Fons Schipper1,2, Ruud J G van Sloun1,2, Angela Grassi1
1Philips Research, Eindhoven, The Netherlands.
Physiological Measurement
|March 19, 2021
Summary
A novel chest-worn accelerometer accurately measures respiratory rate and effort during sleep, even with varied positions. This non-intrusive method shows promise for diagnosing sleep apnea and monitoring patient health.
Area of Science:
- Biomedical Engineering
- Physiological Monitoring
- Wearable Technology
Background:
- Respiratory rate and effort are crucial for diagnosing sleep apnea and detecting patient deterioration.
- Chest-worn accelerometers offer a potentially easy and non-intrusive method for respiratory monitoring.
- Existing methods may struggle with accuracy and robustness under realistic sleeping conditions, including variable sensor positions and body postures.
Purpose of the Study:
- To investigate a novel method using chest-worn accelerometry for accurate and robust measurement of respiratory rate and effort.
- To evaluate the performance of this method under realistic sleeping conditions with varying sensor positions and body postures.
- To develop a reliable system for respiratory monitoring that is non-intrusive and easy to use.
Main Methods:
- Twenty subjects (aged 46-65) wore a chest accelerometer and a respiratory impedance plethysmography band as reference.
- Subjects underwent a 90-minute protocol with varied postures and sensor placements.
- A constrained, recursive principal component analysis (PCA) was employed to estimate respiratory effort, and frequency aggregation with a quality index was used for respiratory rate estimation.
Main Results:
- Constrained and recursive PCA significantly reduced estimation error for respiratory effort.
- A trade-off between coverage and limits of agreement was established for respiratory rate.
- Achieving 80% coverage maintained limits of agreement below 1.45 breaths/min; constraining limits to 0.2 breaths/min yielded 5% coverage.
Conclusions:
- Chest-worn accelerometry, using the novel PCA method, is a robust and accurate approach for measuring respiratory features.
- This technology can function effectively under realistic conditions, including changes in sensor position and body posture.
- The findings support the potential of this non-intrusive method for clinical applications like sleep apnea diagnosis and patient monitoring.
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