作者: J.-X. Wu , C.-H. Lin , Y.-C. Du , T. Chen
DOI: 10.1049/IET-SMT.2011.0002
关键词: Diabetic foot 、 Medicine 、 Vascular occlusive disease 、 Internal medicine 、 Claudication 、 Peripheral 、 Vascular disease 、 Photoplethysmogram 、 Blood volume 、 Classifier (UML) 、 Cardiology
摘要: This study proposes a method for lower limb peripheral vascular occlusive disease (PVOD) estimation using Sprott chaos synchronisation (CS) classifier. Early PVOD is important the patients to prevent ischaemic chest pain and disabling claudication. Photoplethysmography (PPG) non-invasive technique detect blood volume changes in arteries. The pulse transit time increases with severity, normalised amplitudes decrease disease. Synchronous PPG pulses gradually become asynchronous producers at right left sites. A CS detector based on system used track bilateral similarity or asymmetry of signals, construct various butterfly motion patterns. An adaptive classifier wolf pack search algorithm performs estimate grade PVOD, including normal condition, lower-grade higher-grade For 21 subjects, proposed demonstrates greater efficiency higher accuracy estimation.