Figure 2.

Learning preprocessing. A spline curve was created to eliminate noise from one auscultation sound. We extracted a number of convex curve ranges from the created spline curve with a duration of 0.5–2.0 s. The result was estimated to include an arteriovenous fistula sound equivalent to one heartbeat. The sound of one beat of the arteriovenous fistula was extracted by a deep learning classifier. Arteriovenous fistula sounds of 10,000 beats were classified into one of five types (i.e., normal sound, hard sound, high sound, intermittent sound, and whistling).