Key result
A Kalman smoother method fusing modulation signals based on respiratory quality indices outperformed existing methods for breathing rate estimation from ECG and PPG signals.
Why the study?
Accurate breathing rate estimation using only ECG or PPG signals was needed to avoid wearing cumbersome and uncomfortable sensors for direct measurements.
Does a Kalman smoother fusion algorithm using ECG or PPG signals improve the accuracy of breathing rate estimation compared to existing methods?
Does a Kalman smoother fusion algorithm using ECG or PPG signals improve the accuracy of breathing rate estimation compared to existing methods?
A novel Kalman smoother fusion algorithm using ECG or PPG signals provides more accurate breathing rate estimation than existing methods, potentially avoiding the need for cumbersome direct measurement sensors.
May enhance non-invasive respiratory monitoring via ECG/PPG; leaves open prospective clinical validation.
OBJECTIVE: The objective of this paper is to obtain accurate estimation of breathing rate (BR), using only the electrocardiogram (ECG) or the photoplethysmogram (PPG) signals, to avoid wearing cumbersome and uncomfortable sensors for direct measurements. METHODS: Several respiration waveforms are derived from ECG or PPG signals based on amplitude, frequency, and baseline wander modulations. It is, however, difficult to determine their optimal combination for BR estimation due to the noise and patient specificity. We first propose to quantify the quality of modulation waveforms using respiratory quality indices (RQIs). We then present two methods: the first automatically selects the modulation signal with highest RQI for BR estimation, and the second tracks the respiration signal using the Kalman smoother to fuse modulation signals with highest RQI. RESULTS: These two methods are evaluated on two independent datasets, one benchmark database (DB) with immobilized patients recordings and the second with those performing daily activities. Our results outperform existing methods in the literature in both the cases. CONCLUSION: Experimental results show that the RQIs coupled with a fusion algorithm increases the accuracy for BR estimations in dealing with derived modulation signals. SIGNIFICANCE: This work describes a robust Kalman Smoother method applicable in multiple clinical contexts to improve breathing rate estimation from data fusion.
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Khreis et al. (2019) studied Breathing rate estimation. Kalman smoother with respiratory quality indices (RQIs) vs. Existing methods in the literature was evaluated on Accuracy of breathing rate estimation. A Kalman smoother method fusing modulation signals based on respiratory quality indices outperformed existing methods for breathing rate estimation from ECG and PPG signals.
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