Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals

Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals Md Nazmul Islam Shuzan Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia http://orcid.org/0000-0002-3089-9737 Moajjem Hossain Chowdhury Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia Muhammad E. H. Chowdhury Department of Electrical Engineering, Qatar University, Doha 2713, Qatar http://orcid.org/0000-0003-0744-8206 Murugappan Murugappan Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology and Advanced Studies, Chennai 600117, Tamil Nadu, India Center for Excellence for Unmanned Aerial Systems, Universiti Malaysia Perlis, Perlis 02600, Malaysia http://orcid.org/0000-0002-5839-4589 Enamul Hoque Bhuiyan BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA http://orcid.org/0000-0002-3098-1095 Mohamed Arslane Ayari Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar http://orcid.org/0000-0002-8663-886X Amith Khandakar Department of Electrical Engineering, Qatar University, Doha 2713, Qatar http://orcid.org/0000-0001-7068-9112

The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.