Research on the Filter Design for Oximetry Monitors

Blood oxygen saturation is an important parameter of the respiratory system, and its monitoring is crucial during clinical ambulance operations, but the accuracy and integration of current oximeters need to be improved. In order to cope with the inevitable noise interference in the oximetry detection, the current oximetry monitors often use Kalman filtering FIR filtering, and other algorithms to optimize the detection signal. However, these filters have certain limitations in dealing with ambient light changes and different scenarios. For this reason, this paper proposes an adaptive filtering algorithm. In this algorithm, in a specific signal - processing situation, the noise signal is superimposed on the original signal after the unknown system undergoes a linear change, thus forming the desired signal. The noise signal enters the adaptive filter as an input, and the adaptive algorithm inside the filter makes the noise signal as close as possible to the state of the unknown system in the natural environment and generates an output signal. The noise in the desired signal is canceled out by the negative feedback mechanism and an error signal is obtained. The error signal is fed back to the adaptive filter, which updates its weights and then continues with the next filtering operation, and so on to achieve continuous optimization of the signal.