Key result
The proposed ensemble-based gradient boosting algorithm estimated respiratory rate with a mean absolute error of 3.30 bpm, compared to 4.82 bpm for LSTM and 5.83 bpm for SVR.
Why the study?
Current hospital environments suffer from inaccurate respiratory monitoring, and few studies have applied machine learning techniques to photoplethysmography-based respiratory rate estimation.
Absolute Event Rate: 3.3% vs 4.82%
An ensemble gradient boosting algorithm using photoplethysmography provides more accurate respiratory rate estimation compared to other machine learning techniques.
Outperforms comparators in respiratory rate estimation accuracy; leaves open prospective clinical validation before adoption.
Estimating the correct respiratory rate (RR) is an essential technique for intensive care units, hospitals, geriatric hospital facilities, and home care services. Capnography is a standard methodology used to monitor carbon dioxide concentrations or partial pressures of respiratory gases to provide the most accurate RR measurements. However, it is inconvenient to use and has been primarily used while administering anesthesia and during intensive care. Many researchers now use electrocardiogram signals to estimate RR. Despite the recent developments, the current hospital environments suffer from inaccurate respiratory monitoring. While various machine learning techniques, including deep learning, have recently been applied to the medical processing sector, only a few studies have been conducted in the field of RR estimation. Therefore, using photoplethysmography, machine-learning techniques such as the ensemble gradient boosting algorithm are being employed in RR estimation. Multi-phases are used based on various feature extraction and selection methodology to improve the performance for RR estimation. In this study, the number of ensembles is increased, and the proposed ensemble methodology is effectively learned to estimate the RR. The proposed ensemble-based gradient boosting algorithm are compared with those of ensemble-based long-short memory network, and ensemble-based supported vector regression techniques, 3.30 breaths per min (bpm), 4.82 bpm and 5.83 bpm based on mean absolute errors. The proposed method shows a more accurate estimate of the respiration rate.
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Lee et al. (2020) studied Respiratory rate estimation. Ensemble-based gradient boosting algorithm using photoplethysmography vs. Ensemble-based long-short memory network and supported vector regression was evaluated on Mean absolute error of respiratory rate estimation. The proposed ensemble-based gradient boosting algorithm estimated respiratory rate with a mean absolute error of 3.30 bpm, compared to 4.82 bpm for LSTM and 5.83 bpm for SVR.
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