  Objective: Syncope is a sudden loss of consciousness with loss of postural
tone and spontaneous recovery; it is a common condition, albeit one that is
challenging to accurately diagnose. Uncertainties about the triggering
mechanisms and their underlying pathophysiology have led to various
classifications of patients exhibiting this symptom. This study presents a new
way to classify syncope types using machine learning. Method: we hypothesize
that syncope types can be characterized by analyzing blood pressure and heart
rate time series data obtained from the head-up tilt test procedure. By
optimizing classification rates, we identify a small number of determining
markers which enable data clustering. Results: We apply the proposed method to
clinical data from 157 subjects; each subject was identified by an expert as
being either healthy or suffering from one of three conditions:
cardioinhibitory syncope, vasodepressor syncope and postural orthostatic
tachycardia. Clustering confirms the three disease groups and identifies two
distinct subgroups within the healthy controls. Conclusion: The proposed method
provides evidence to question current syncope classifications; it also offers
means to refine them. Significance: Current syncope classifications are not
based on pathophysiology and have not led to significant improvements in
patient care. It is expected that a more faithful classification will
facilitate our understanding of the autonomic system for healthy subjects,
which is essential in analyzing pathophysiology of the disease groups.
