1 research outputs found
Robust Modelling of Reflectance Pulse Oximetry for SpO Estimation
Continuous monitoring of blood oxygen saturation levels is vital for patients
with pulmonary disorders. Traditionally, SpO monitoring has been carried
out using transmittance pulse oximeters due to its dependability. However,
SpO 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 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
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 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.Comment: Accepted in EMBC 2020(EMBS