  The first objective of this paper is to introduce a unified approach to the
D/A conversion, a real-time algorithm referred to as {\it blending operator},
based on spline functions of arbitrarily desired order, to interpolate the
irregular data samples, while preserving all polynomials of the same spline
order, with assured maximum order of approximation. This helps remove the two
main obstacles for adapting the recently proposed time-frequency analysis
technique {\it Synchrosqueezing transform} (SST) to irregular data samples in
order to allow online computation. Secondly, for real-time dynamic information
extraction from an oscillatory signal via SST, a family of vanishing-moment and
minimum-supported spline-wavelets (to be called VM wavelets) are introduced for
on-line computation of the CWT and its derivative. The second objective of this
paper is to apply the proposed real-time algorithm and VM wavelets to clinical
applications, particularly to the study of the "anesthetic depth" of a patient
during surgery, with emphasis on analyzing two dynamic quantities: the
"instantaneous frequencies" and the "non-rhythmic to rhythmic ratios" of the
patient's respiration, based on a one-lead electrocardiogram (ECG) signal.It is
envisioned that the proposed algorithm and VM wavelets should enable real-time
monitoring of "anesthetic depth", during surgery, from the respiration signal
via ECG measurement.
