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

ABSTRACT: 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.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.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 mortality for sepsis patients, and found it had greater prognostic accuracy than standard clinical severity scores.we developed a novel framework to classify multiscale features of beat-to-beat dynamics, and performed an initial clinical validation to demonstrate that our approach generates a robust quantification of a patient's state, compatible with real-time bedside implementations.the framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of "always-on" diagnostic tools.

SUBMITTER: Vandendriessche B

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

REPOSITORIES: biostudies-literature

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

Vandendriessche Benjamin B Abas Mustafa M Dick Thomas E TE Loparo Kenneth A KA Jacono Frank J FJ

IEEE transactions on bio-medical engineering 20170317 12

<h4>Objective</h4>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.<h4>Methods</h4>an artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. The results a ...[more]

PMID: 28328498

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Dataset Information

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

Publications

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

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