Assessment of peripheral vascular occlusive disease using adaptive network-based fuzzy inference system

Abstract

This paper proposes the assessment of diabetic foot using adaptive network-based fuzzy inference system (ANFIS). Diabetic foot occurs due to peripheral vascular occlusive disease (PVOD) and leads to disable claudication and gangrene. According to previous study, the transit timing, shape waveforms, and normalized amplitudes of photoplethysmography (PPG) signals tend to increase with PVOD severity. An ANFIS is proposed to assess PVOD using the absolute bilateral differences of the timing parameters ΔPTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> , ΔPTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> , and ΔRT. For twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups, the results will show high accuracy for PVOD assessment.