Noninvasive radial pressure waveform estimation by transfer functions using particle swarm optimization

Waveforms of blood pressure contain very important signals of life. Although blood pressure can be continuously measured by an intra arterial catheter, this invasive method introduces risks to patients. Knowing that blood pressure can change in just a few seconds or minutes without a sensible feeling, the waveforms of blood pressure are capable of conveying substantial cardiovascular information. Traditional Chinese medicine also uses radial pressure information in the form of pulses to diagnose diseases by sensing the signals from the fingertips. Therefore, a noninvasive method in measuring blood pressure waveforms is proposed in this paper, based on which we can use the signals of fingertip photoplethysmogram to reconstruct radial pressure waveforms. Characteristics of various photoplethysmogram will be categorized into 3 clusters by using fuzzy C-mean clustering. A particle swarm optimization scheme is then esblihed to search for an optimal transfer function model for estimating the radial pressure waveforms. Experiment results show that correlation ratio of the transformed waveforms can be as high as 0.89, much better than the results via the ARX technique.