Cardiac Phase Space Analysis: Assessing Coronary Artery Disease Utilizing Artificial Intelligence

Abstract

The bridge of artificial intelligence to cardiovascular medicine has opened up new avenues for novel diagnostics that may significantly enhance the cardiology care pathway. Cardiac phase space analysis is a noninvasive diagnostic platform that combines advanced disciplines of mathematics and physics with machine learning. Thoracic orthogonal voltage gradient (OVG) signals from an individual are evaluated by cardiac phase space analysis to quantify physiological and mathematical features associated with coronary stenosis. The analysis is performed at the point of care without the need for a change in physiologic status or radiation. This review will highlight some of the scientific principles behind the technology, provide a description of the system and device, and discuss the study procedure, clinical data, and potential future applications.

Figure 1

Data acquisition setup: (a) signal acquisition device and (b) patient and electrodes/lead placement and PPG configurations.

Figure 2

Phase Space (PS) Residues from a CAD positive subject and CAD negative subject. The PS Residues are 3D computation objects generated from the difference of the actual signal from the modelled signal in three dimensions. These objects can be evaluated geometrically to produce features (such as surface area or volume). The coloring can represent another measurable dimension. Here, the images are colored by where in the depolarization/repolarization cycle the point difference comes from. The top image is a single projection of the 3D PS Residue image. The 6 smaller projections are different views of the larger object.