Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease

Early detection of lower limb peripheral vascular occlusive disease (PVOD) is important to prevent patients from getting disabled claudication, ischemic rest pain and gangrene. This paper proposes a method for the estimation of lower limb PVOD using chaos synchronization (CS) detector with synchronous photoplethysmography (PPG) signal recorded from the big toes of both right and left feet for 21 subjects. The pulse transit time of PPG increases with diseased severity and the normalized amplitudes decreases in vascular disease. Synchronous PPG pulses acquired at the right and left big toes gradually become asynchronous as the disease progresses. A CS detector is used to track bilateral similarity or asymmetry of PPG signals, and to construct various butterfly motion patterns. Artificial neural network (ANN) was used as a classifier to classify and assess the PVOD severity. The results demonstrated that the proposed method has great efficiency and high accuracy in PVOD estimation.