A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features.

ABSTRACT:

Objective

state-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside.

Methods

an artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. The results are mapped into a physiologic state space for near real-time patient-state tracking.

Results

the framework was validated on cardiac beat-to-beat dynamics processed with the multiscale entropy algorithm, and assessed using PhysioNet databases. We then applied our algorithm to predict 28-day mor

SUBMITTER: Vandendriessche B 

PROVIDER: S-EPMC5736792 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature