The Detection of Arteriovenous Fistula Stenosis for Hemodialysis Based on
Wavelet Transform
血液透析動靜脈瘻管狹窄小波檢測Yen-Nien Wang, Chih-Yang Chan, & Shi-Jun Chou
International Journal of Advanced Computer Science, Vol. 1, No. 1, Pp. 16-22, Jul. 2011.
Presenter: Wei-Liang Hong
Professor: Dr. Chun-Ju Hou
12012/02/17
Outline
• Foreword• Methods• Results and compare• Conclusions• Future work• References
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Number of patients with end-stage renal disease
• The USRDS dialysis statistics in 2008 year– Taiwan No.1–Much higher than Japan and USA
• Statistics of Department of Health Executive Yuan in 1999– There are 67,356 dialysis patients
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Methods to treat renal disease
• Kidney transplantation• Hemodialysis– Heritoneal dialysis 9.17%– Hemodialysis 90.83%• Autogenous arteriovenous fistula• Artificial fistula• U-type transplanted fistula、 linear transplanted fistula
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Vascular stenosis
• High blood fat or cholesterol– Vascular stenosis– Coma, shock or death
• Sea-gull Murmur– 250~1KHz
• Inadequate flow of not function properly dialysis– 400cm/sec
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Methods
• Auscultation• Wavelet transform • STFT• FFT• Hilbert transform
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Wavelet transform
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Software flowchart
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Ultrasound Fig
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Time-Frequency Analysis
(a) Stenosis-free representation;(b) Stenosis representation. 10
Time-Frequency AnalysisStenosis-free
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Stenosis
STFT
Stenosis-free
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Stenosis
FFT
Stenosis-free
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Stenosis
Hilbert transform
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StenosisStenosis-free
Results of the comparison
Sequence Comparison with ultrasonic detection results
Numberof datagroups
Percentage oftotal data
A Not consistent 12 14.45%
B Consistent 71 85.54%
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Conclusions
• Hilbert transform• Eigenvalue of 700~800Hz
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Future work
• Measured patient data and compare• This paper analysis the reference of method• The production of the hardware circuit
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References
• 衛生署 ,”99年醫療統計年報”• 衛生署 ,”國際資料比較”• 知識庫 ,”最新研究報告”• Yen-Nien Wang, Chih-Yang Chan, & Shi-Jun
Chou, “The Detection of Arteriovenous Fistula Stenosis for Hemodialysis Based on Wavelet Transform,” (Jul. 2011.) International Journal of Advanced Computer Science, Vol. 1, No. 1, pp. 16-22
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Thank you for your listening!
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