  Respiratory ailments such as asthma, chronic obstructive pulmonary disease
(COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR)
is a vital indicator of the wellness of a patient. Continuous monitoring of RR
can provide early indication and thereby save lives. However, a real-time
continuous RR monitoring facility is only available at the intensive care unit
(ICU) due to the size and cost of the equipment. Recent researches have
proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR
estimation however, the usage of ECG is limited due to the unavailability of it
in wearable devices. Due to the advent of wearable smartwatches with built-in
PPG sensors, it is now being considered for continuous monitoring of RR. This
paper describes a novel approach to RR estimation using machine learning (ML)
models with the PPG signal features. Feature selection algorithms were used to
reduce computational complexity and the chance of overfitting. The best ML
model and the best feature selection algorithm combination was fine-tuned to
optimize its performance using hyperparameter optimization. Gaussian Process
Regression (GPR) with fitrgp feature selection algorithm outperformed all other
combinations and exhibits a root mean squared error (RMSE), mean absolute error
(MAE), and two-standard deviation (2SD) of 2.57, 1.91, and 5.13 breaths per
minute, respectively. This ML model based RR estimation can be embedded in
wearable devices for real-time continuous monitoring of the patient.
