Key result
Combining Gaussian process regression and spectral generation yields ~0.8 brpm error for PPG respiratory rate.
Why the study?
Biosignal data imbalance between normal breathing and less frequent states like dyspnea and hypopnea degrades machine learning algorithm accuracy when estimating respiratory rates from photoplethysmography signals.
The proposed GPR-IPSG model addresses data imbalance in biosignals, improving the accuracy and stability of respiratory rate estimation from photoplethysmography signals for home-based monitoring systems.
Answers are grounded in this paper and cite their sources.
May facilitate home-based PPG respiratory monitoring; hypothesis-generating pending prospective validation.
Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimation of RRs for cardiopulmonary function is essential for home health monitoring systems. However, machine learning (ML) algorithm errors embedded in health monitoring systems can be problematic in medical decision-making because some data have much larger sample sizes in the training set than others. This difference in sample size implies biosignal data imbalance. Therefore, we propose a novel methodology that combines bootstrap-based imbalanced continuous power spectral generation (IPSG) with ML approaches to estimate RRs and uncertainty to address data imbalance. The sample differences between normal breathing (12-20 breaths per minute (brpm)), dyspnea (≥20 brpm), and hypopnea (<8 brpm) show significant data imbalance, which can affect the learning of ML algorithms. Hence, the normal breathing part with a large amount of data is well-trained. In contrast, the dyspnea and hypopnea parts with relatively fewer data are not well-trained, and this data imbalance makes it difficult to estimate the reference variables of the actual dyspnea and hypopnea data parts, thus generating significant errors. Hence, we apply ML models by mixing artificial feature curves generated using a bootstrap model with the original feature curves to estimate RRs and solve this problem. As a result, the nonparametric bootstrap approach significantly increases the number of artificial feature curves. The generated artificial feature curves are selectively utilized in the highly imbalanced parts. Therefore, we confirm that IPSG is efficiently trained to predict the complex nonlinear relationship between the feature vectors obtained from the photoplethysmography signal and the reference RR. The proposed methodology shows more accurate prediction performance and uncertainty. Combining the proposed Gaussian process regression (GPR) with IPSG based on the Beth Israel Deaconess Medical Center dataset, the mean absolute error of the RR is 0.79 and 1.47 brpm. Our approach achieves high stability and accuracy by randomly mixing original and artificial feature curves. The proposed GPR-IPSG model can improve the performance of clinical home-based monitoring systems and design a reliable framework.
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Lee et al. (2025) studied Respiratory rate estimation. Gaussian process regression (GPR) with imbalanced continuous power spectral generation (IPSG) was evaluated on Mean absolute error of the respiratory rate. Combining Gaussian process regression with imbalanced continuous power spectral generation achieved a mean absolute error of 0.79 and 1.47 brpm for respiratory rate estimation from PPG signals.
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