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Empirical wavelet transform-based delineator for arterial blood pressure waveforms

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Arterial blood pressure (ABP) waveforms provide plenty of pathophysiological information about the cardiovascular system. ABP pulse analysis is a routine process used to investigate the health status of the cardiovascular system. ABP pulses correspond to the contraction and relaxation phenomena of the human heart. The contracting or pumping phase of the cardiac chamber corresponds to systolic pressure, whereas the resting or filling phase of the cardiac chamber corresponds to diastolic pressure. An ABP waveform commonly comprises systolic peak, diastolic onset, dicrotic notch, and dicrotic peak. Automatic ABP delineation is extremely important for various biomedical applications. In this paper, a delineator for onset and systolic peak detection in ABP signals is presented. The algorithm uses a recently developed empirical wavelet transform (EWT) for the delineation of arterial blood pulses. EWT is a new mathematical tool used to decompose a given signal into different modes and is based on the design of an adaptive wavelet filter bank. The performance of the proposed delineator is evaluated and validated over ABP waveforms of standard databases, such as the MIT-BIH Polysomnoghaphic Database, Fantasia Database, and Multiparameter Intelligent Monitoring in Intensive Care Database. In terms of pulse onset detection, the proposed delineator achieved an average error rate of 0.11%, sensitivity of 99.95%, and positive predictivity of 99.92%. In a similar manner for systolic peak detection, the proposed delineator achieved an average error rate of 0.10%, sensitivity of 99.96%, and positive predictivity of 99.92%.
Rocznik
Strony
61--66
Opis fizyczny
Bibliogr. 16 poz., rys., wykr.
Twórcy
autor
  • Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144 011, India
  • Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144 011, India
Bibliografia
  • 1. Zong W, Hedlt T, Moody GB, Mark RG. An open source algorithm to detect onset of arterial blood pressure pulses. Comput Cardiol 2003;30:259–62.
  • 2. Anonelli L, Ohley W, Khamlach R. Dicrotic notch detection using wavelet transform analysis. In: Proceeding of 16th Annual International Conference on IEEE Engineering in Medicine and Biology Society, vol. 2, 1994:1216–7.
  • 3. Kinias PF, Norusis M. A real time pressure algorithm. Comput Biol Med 1981;11:211–20.
  • 4. Aboy M, McNames J, Thong T, Tsunami D, Ellenby MS, Goldstein B. An automatic beat detection algorithm for pressure signals. IEEE Trans Biomed Eng 2005;52:1662–70.
  • 5. Aboy M, McNames J, Goldstein B. Automatic detection algorithm of intracranial pressure waveform components. In: Proceedings of 23rd International Conference on IEEE Engineering in Medicine and Biology Society, vol. 3, 2001:2231–4.
  • 6. Antonelli L, Khanmlach R. Wavelet transform analysis of the arterial pressure waveform. Comput Cardiol 1994;21:568–71.
  • 7. Navakatikyan MA, Barrett CJ, Head GA, Ricketts JH, Malpas SC. A real-time algorithm for the quantification of blood pressure waveforms. IEEE Trans Biomed Eng 2002;49:662–70.
  • 8. Lehmann ED, Hopkins KD, Golsling RG. Assessment of arterial distensibility by automatic pulse wave velocity measurement. Hypertension 1996;27:1188–91.
  • 9. Li BN, Dong MC, Vai MI. On an automatic delineator for arterial blood pressure waveforms. Biomed Signal Process Control 2010;5:76–81.
  • 10. Li BN, Fu BB, Dong MC. Development of a mobile pulse waveform analyzer for cardiovascular health monitoring. Comput Biol Med 2008;38:438–45.
  • 11. Oppenheim MI, Sittig DF. An innovative dicrotic notch detection algorithm which combines rule-based logic with digital signal processing techniques. Comput Biomed Res 1995;28:154–70.
  • 12. Hoeksel SAAP, Jansen JRC, Blom JA, Schreuder JJ. Detection of dicrotic notch in arterial pressure signals. J Clin Monitor 1997;13:309–16.
  • 13. Mallat S, Hwang WL. Singularity detection and processing with wavelets. IEEE Trans Inf Theory 1992;38:617–43.
  • 14. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode composition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 1998;454:903–95.
  • 15. Flandrin P, Rilling G, Gonçalvés P. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 2004;11:112–4.
  • 16. Gilles J. Empirical wavelet transform. IEEE Trans Signal Process 2013;61:3999–4010.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ab398a9c-4115-4624-be34-bb5632758f5b
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