Robust Modelling of Reflectance Pulse Oximetry for SpO_2 Estimation
share
Continuous monitoring of blood oxygen saturation levels is vital for patients with pulmonary disorders. Traditionally, SpO_2 monitoring has been carried out using transmittance pulse oximeters due to its dependability. However, SpO_2 measurement from transmittance pulse oximeters is limited to peripheral regions. This becomes a disadvantage at very low temperatures as blood perfusion to the peripherals decreases. On the other hand, reflectance pulse oximeters can be used at various sites like finger, wrist, chest and forehead. Additionally, reflectance pulse oximeters can be scaled down to affordable patches that do not interfere with the user's diurnal activities. However, accurate SpO_2 estimation from reflectance pulse oximeters is challenging due to its patient dependent, subjective nature of measurement. Recently, a Machine Learning (ML) method was used to model reflectance waveforms onto SpO_2 obtained from transmittance waveforms. However, the generalizability of the model to new patients was not tested. In light of this, the current work implemented multiple ML based approaches which were subsequently found to be incapable of generalizing to new patients. Furthermore, a minimally calibrated data driven approach was utilized in order to obtain SpO_2 from reflectance PPG waveforms. The proposed solution produces an average mean absolute error of 1.81% on unseen patients which is well within the clinically permissible error of 2%. Two statistical tests were conducted to establish the effectiveness of the proposed method.
READ FULL TEXT