Key result
A novel sparse signal reconstruction framework accurately monitored respiratory rate from the photoplethysmogram, achieving an overall root mean squared error of 3.25 breaths/min.
Population
Public benchmark database (Capnobase) containing photoplethysmogram (PPG) signals
Comparison
Sparse signal reconstruction framework with… vs State-of-the-art algorithm
Design
Other
Authors
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Requires prospective validation before clinical use; leaves open accuracy in diverse patient populations.
A novel sparse signal reconstruction framework accurately monitors respiratory rate from PPG signals even at a low sampling frequency of 10 Hz, enabling potential use in low-cost wearable devices.
Zhang et al. (2016) studied Respiratory rate monitoring. Sparse signal reconstruction (SSR) framework vs. State-of-the-art algorithm was evaluated on Root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of respiratory rate estimates. A novel sparse signal reconstruction framework accurately monitored respiratory rate from the photoplethysmogram, achieving an overall root mean squared error of 3.25 breaths/min.
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