According to a study published in the Journal of Applied Physics, researchers from the University of Kerala and the University of Nova Gorica, Slovenia, developed a method to identify valve dysfunction using complex network analysis.

The researchers explained that aortic valve stenosis occurs when the aortic valve narrows, constricting blood flow from the heart through the artery and to the entire body. In severe cases, it can lead to heart failure. They stated that the diagnostic tool works based on the sounds produced by the heart. The heart sound was recorded for over 10 minutes to create a graph or a complex network of connected points.

The data was split into sections, and each part was represented by a node or single point on the graph. If the sound in that portion of the data was similar to another section, a line, or edge, was drawn between the two nodes.

The analysis of the graph points showed that in a healthy heart, the graph showed two distinct clusters of points, with many nodes unconnected. In contrast, a heart with aortic stenosis contained many more correlations and edges. The researchers used machine learning to examine the graphs and identify those with and without disease, achieving a classification accuracy of 100%. 

(Source: https://theprint.in/health/low-cost-method-developed-to-diagnose-valve-dysfunction-in-heart/1224242/)