TY - GEN
T1 - Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease
AU - Lin, Chia Hung
AU - Chen, Yung Fu
AU - Du, Yi Chun
AU - Wu, Jian Xing
AU - Chen, Tainsong
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-642-13923-9_13
DO - 10.1007/978-3-642-13923-9_13
M3 - Conference contribution
AN - SCOPUS:77954638232
SN - 3642139221
SN - 9783642139222
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 126
EP - 136
BT - Medical Biometrics - Second International Conference, ICMB 2010, Proceedings
T2 - 2nd International Conference on Medical Biometrics, ICMB 2010
Y2 - 28 June 2010 through 30 June 2010
ER -