Sprott chaos synchronisation classifier for diabetic foot peripheral vascular occlusive disease estimation

This study proposes a method for lower limb peripheral vascular occlusive disease (PVOD) estimation using a Sprott chaos synchronisation (CS) classifier. Early PVOD estimation is important for the patients to prevent ischaemic chest pain and disabling claudication. Photoplethysmography (PPG) is a non-invasive technique to detect blood volume changes in peripheral arteries. The pulse transit time increases with disease severity, and normalised amplitudes decrease in vascular disease. Synchronous PPG pulses gradually become asynchronous producers at the right and left sites. A CS detector based on the Sprott system is used to track bilateral similarity or asymmetry of PPG signals, and to construct various butterfly motion patterns. An adaptive classifier with wolf pack search algorithm performs to estimate the grade of PVOD, including normal condition, lower-grade disease and higher-grade disease. For 21 subjects, the proposed method demonstrates greater efficiency and higher accuracy in PVOD estimation.