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

Chia Hung Lin, Yung Fu Chen, Yi Chun Du, Jian Xing Wu, Tainsong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMedical Biometrics - Second International Conference, ICMB 2010, Proceedings
Pages126-136
Number of pages11
DOIs
Publication statusPublished - 2010 Jul 21
Event2nd International Conference on Medical Biometrics, ICMB 2010 - Hong Kong, China
Duration: 2010 Jun 282010 Jun 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6165 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Medical Biometrics, ICMB 2010
CountryChina
CityHong Kong
Period10-06-2810-06-30

Fingerprint

Chaos Synchronization
Photoplethysmography
Chaos theory
Synchronization
Detector
Detectors
Pain
Asymmetry
Artificial Neural Network
High Accuracy
Classifiers
Classify
Classifier
Neural networks
Decrease
Motion

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lin, C. H., Chen, Y. F., Du, Y. C., Wu, J. X., & Chen, T. (2010). Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease. In Medical Biometrics - Second International Conference, ICMB 2010, Proceedings (pp. 126-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6165 LNCS). https://doi.org/10.1007/978-3-642-13923-9_13
Lin, Chia Hung ; Chen, Yung Fu ; Du, Yi Chun ; Wu, Jian Xing ; Chen, Tainsong. / Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease. Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. 2010. pp. 126-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "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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Lin, CH, Chen, YF, Du, YC, Wu, JX & Chen, T 2010, Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease. in Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6165 LNCS, pp. 126-136, 2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, China, 10-06-28. https://doi.org/10.1007/978-3-642-13923-9_13

Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease. / Lin, Chia Hung; Chen, Yung Fu; Du, Yi Chun; Wu, Jian Xing; Chen, Tainsong.

Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. 2010. p. 126-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6165 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lin CH, Chen YF, Du YC, Wu JX, Chen T. Chaos synchronization detector combining radial basis network for estimation of lower limb peripheral vascular occlusive disease. In Medical Biometrics - Second International Conference, ICMB 2010, Proceedings. 2010. p. 126-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-13923-9_13