Real-time dynamics acquisition from irregular samples -- with application to anesthesia evaluation

TL;DR

This paper introduces a unified spline-based real-time algorithm for irregular data interpolation and applies it to analyze anesthesia depth through ECG-derived respiration signals, enabling online monitoring during surgery.

Contribution

It develops a novel blending operator for real-time irregular data interpolation and introduces VM wavelets for efficient online time-frequency analysis in clinical settings.

Findings

Effective real-time interpolation of irregular samples achieved.

Successful application to anesthesia depth monitoring during surgery.

Enhanced analysis of respiratory dynamics from ECG signals.

Abstract

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.