Signal Processing Method for Enhancement of Pulse Oximeter Signals

Pulse oximeters are now a part of every household first-aid kit, pulse oximeters have actually helped to primarily identify the severity of covid19 infection in a person’s body. These devices measure the saturated blood oxygen level (SpO
2
) in a person’s body, there by the displayed level of SpO
2
helps medical professionals to hypothesize the situation and provide a better aid for the patient. Since the process is non-invasive, the devices are widely implemented. Pulse oximeters acquire photoplethysmographic (PPG) signals, these signals contain the volumetric changes in human blood, that on being exposed to mathematical principles give the SpO
2
reading and other data. The process of obtaining the PPG signals through pulse oximetry employs a mechanism of emitting and detecting the IR and Red signals through human tissues, however during the capturing of reflected signals through detector, the detected signal comes along with noise referred as motion artifact (MA). These MAs arises due to the voluntary/involuntary movements of human causing volumetric changes in flow of blood at the source and detector sensor locations. The presence of MAs in such signals turns up to erroneous SpO
2
level estimation, that creates a problem for medical professionals in treating the diseases. To improve the reliability of SpO2 estimation, by a pulse oximeter, the PPG signal quality is to be enhanced. In this paper, the authors tried to describe on the work of enhancing the acquired PPG signal quality by reducing MAs with effective methods.