Digital models for arterial pressure and respiratory waveforms

Digital models for arterial pressure pulse (APP) and respiratory volume waveforms (RVW) are proposed for efficient representation of these signals. When these signals are discrete cosine transformed (DCT), the pole-zero technique of Steiglitz-McBride (SM) (1965) gives system functions of much lower order than those obtained directly from the signals. The DCT of a bell-shaped biphasic wave needs two poles and two zeros, so the model order is fixed by the number of distinct peaks in the magnitude spectrum of the transformed APP/RVW signal. The partial fraction expansion (PFE) of the system function allows delineation of component waves present in the time signal. The angles of model poles and zeros enable easy determination of several important features from both of these signals. The model performance is evaluated using the normalized root mean-square error. A Bayes classifier using the pole angles as the feature vector performs satisfactorily when a limited number of RVWs recorded under deep and rapid maneuver are classified as normal and abnormal respiratory pathways.