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

Area of Science:

• Physiological signal processing
• Artificial intelligence in medicine
• Nonlinear dynamics

Background:

• Advanced algorithms for quantifying nonlinear dynamics in physiological waveforms are underutilized clinically due to their complexity.
• There is a need for accessible, real-time patient-state tracking tools at the bedside.

Purpose of the Study:

• To present a generalizable framework for classifying multiscale waveform features for patient-state tracking.
• To enable the clinical application of complex nonlinear dynamics analysis.

Main Methods:

• An artificial neural network classifier was developed to evaluate multiscale waveform features against a synthetic time series database.
• The framework was validated using cardiac beat-to-beat dynamics and PhysioNet databases.
• The algorithm was applied to predict 28-day mortality in sepsis patients.

Main Results:

• The framework successfully classified multiscale features of beat-to-beat dynamics.
• The approach demonstrated robust quantification of patient state, compatible with real-time bedside implementation.
• The algorithm showed greater prognostic accuracy for sepsis mortality prediction compared to standard clinical severity scores.

Conclusions:

• A novel framework for classifying multiscale features of physiological dynamics was developed and initially validated.
• The approach provides a robust quantification of patient state suitable for real-time bedside applications.
• This framework can facilitate the adoption of advanced "always-on" diagnostic tools.