Track waveform9/1/2023 The framework generates meaningful and actionable patient-specific information, and could facilitate the dissemination of a new class of "always-on" diagnostic tools.Ī) 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 c i, constant to generate equidistantly spaced γ ≥ β ≥ −3. 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. 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. The framework was validated on cardiac beat-to-beat dynamics processed with the multiscale entropy algorithm, and assessed using PhysioNet databases. The results are mapped into a physiologic state space for near real-time patient-state tracking. We present a generalizable framework for classifying multiscalar waveform features, designed for patient-state tracking directly at the bedside.Īn artificial neural network classifier was designed to evaluate multiscale waveform features against a fingerprint database of multifractal synthetic time series. State-of-the-art algorithms that quantify nonlinear dynamics in physiologic waveforms are underutilized clinically due to their esoteric nature.
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