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 mortality for sepsis patients, and found it had greater prognostic accuracy than standard clinical severity scores.

Conclusion: 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.

Significance: the framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of "always-on" diagnostic tools.

Figures

Fig. 1. Schematic representation of the framework

Dark grey boxes: proposed framework workflow. Light grey boxes: specific implementation of the current framework instance. A) Steps to be performed once to set up a new framework instance, see § II. B) Steps to be performed to process new (unknown) data, see § III. iAAFT, iterative Amplitude-Adjusted Fourier Transform.

Fig. 2. Characterization of the FPDB: stochastic vs deterministic properties

A) Schematic representation of the generation of the various types of synthetic time series mat make up the FDPB: Sn, stochastic noise; β, power spectral slope for fractional integration; γ, offset value for slope β; ci, constant to generate equidistantly spaced γ ≥ β ≥ −3. B) From left-to-right: representative tachograms for f−ℚ + Sn99% (approximating white noise, f0); f−ℚ + Sn49%; f−ℚ (multifractal and nonstationary seed time series); f−1.62ℚ (approximating brown noise, f−2); and f2.99ℚ (extreme smoothing, loss of high frequency dynamics). C) Minimum target variance ( σmin∗2) for a representative set of 199 synthetic time series (i.e. encompassing the entire range from f−ℚ + 99% to f−ℚ and f−2.99ℚ determined by delay vector variance (DVV). The lower σmin∗2, the stronger the deterministic component; the higher σmin∗2, the stronger the stochastic component of the time series structure. The middle time series [i = 100] corresponds to a fractional integration slope β of −1. See text for details.

Fig. 3. Characterization of the FPDB: multifractal properties

A) Hurst exponent Hq (fractal dimension) as a function of order q (singularity weighting factor) for a representative set of 199 synthetic time series (i.e. encompassing the entire range from f−ℚ + 99% to f−ℚ and f−2.99ℚ). The annotations correspond to Fig. 2, B. B) Multifractal singularity spectra for representative synthetic time series: αwidth is an estimator of multifractal strength, αmode denotes the most prevalent fractal dimension. The surrogate permutations (randomized (green) and iAAFT (red)) correspond to the original time series (β = −1). See text for details.

Fig. 4. MSE conversion of the FPDB

A) Set of 199 representative synthetic time series converted to their corresponding MSE profiles (see Fig. 3. A). B) Pruning of the MSE profiles to delineate 7 distinct classes. C) Positive (green) and negative (red) shifts of the original (blue) MSE profiles to better match the physiologic range, generating a total of 17 classes. See text for details.

Fig. 5. Schematic representation of the classification scheme and the state space

A) Each class is labeled according to the primary determinant of its structure; (middle column) f−ℚ corresponds to β = −1; f−ℚ + Snx corresponds to increasing amounts (x = 1, 2, 3) of stochastic noise added to the seed time series f−ℚ; fβℚ corresponds to decreasing (x = 1, 2, 3) fractional integration slopes β. The left and right columns represent negative and positive offsets δ, respectively. The drawings are approximations of the MSE profile shape covered by that specific class. B) Visual representation of the state space, overlaid with the 17 classes. The pseudo-coloring delineates multi-class sectors within the state space that share dynamical properties (as quantified by MSE) to improve readability. LRC, long-range correlations; SRC, short-range correlations. See text for details.

Fig. 6. Two representative examples from the NSR database

State space plots for original [top-left] and surrogate [top-right] data, and sector transition plot [bottom] for: 1) dataset #014, NSR; and 2) dataset #007, NSR with marked bradyarrhythmias. See text for details.

Fig. 7. Two representative examples from the Sepsis database

State space plots for original [top-left] and surrogate [top-right] data, and sector transition plot [bottom] for: 1) dataset #032 (“Sepsis – Survived” group); and 2) dataset #046 (“Sepsis – Deceased” group). AKI, Acute Kidney Injury; APACHE II, Acute Physiology and Chronic Health Evaluation; SAPS II, Simplified Acute Physiology Score; CVVH, continuous venovenous hemofiltration. See text for details.

Fig. 8. Relative sector occupancy for the NSR, CHF, and AF databases

A–B) Averaged original (A) and surrogate (B) data for the databases, grouped per sector: f−ℚ, f−βℚ, f−ℚ + Sn, and f0/C. Stars denote comparisons of the CHF, PAF, and SAF groups to the NSR control group. ****, p ≤ 0.0001; ***, p ≤ 0.001; **, p ≤ 0.01; *, p ≤ 0.05.

Fig. 9. Relative sector occupancy for the Sepsis Database

A–B) Averaged original (A) and surrogate (B) data for the “Sepsis - Survived” and “Sepsis - Deceased” groups, grouped per sector: f−ℚ, f−βℚ, f−ℚ + Sn, and f0/C. **, p ≤0.01; *, p ≤ 0.05.