A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features.
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.
Benjamin
B
Vandendriessche
Mustafa
M
Abas
Thomas E
TE
Dick
Kenneth A
KA
Loparo
Frank J
FJ
Jacono
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis; D003422:Critical Care; D016208:Databases, Factual; D004562:Electrocardiography; D005260:Female; D006333:Heart Failure / Q000175:diagnosis; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D017711:Nonlinear Dynamics; D018805:Sepsis / Q000175:diagnosis; D012815:Signal Processing, Computer-Assisted; D000069553:Supervised Machine Learning; D055815:Young Adult