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Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline drift. It is necessary, therefore, to remove the pulse waveform’s baseline drift in order to perform accurate pulse waveform analysis. This chapter presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline drift of pulse waveform. To evaluate the level of baseline drift, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline drift. If the ER is more than a given threshold, the baseline drift can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter, and linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline drift and in preserving the diagnostic information of pulse waveforms. This CAF also can be used to remove the baseline drift of other physiological signals, such as ECG and so on.
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Q. Bian, The Classic of Difficulties (Nan Jing), Tianjing Technology Press, 1979.
O.R. Michael, D.F. Edward, “Pulse pressure: is this a clinically useful risk factor?” Hypertension (1999) 372–374.
Y.W. Ling, “Frequency distribution of human pulse spectra,” IEEE Trans. Biomed. Eng. 32 (1985) 245.
W.A. Lu, Y.Y. Wang, W.K. Wang, “Pulse analysis of patients with severe liver problems,” IEEE Eng. Med. Biol. 18 (1) (1999) 73–75.
J. Ling, D.C. Winter, B.L. Robey, “Cardiac output monitor using fuzzy logic blood pressure analysis,” U.S. Patent No. 6007491, December 1999.
Inukai, et al., “System and method for evaluating the autonomic nervous system of a living subject,” U.S. Patent No. 5830148, November 1998.
M.F. O’Rourke, R.P. Kelly, “Wave reflection in the systemic circulation and its implications in ventricular function,” J. Hypertension 11 (1993) 327–337.
M.F. O’Rourke, J. Lei, D.E. Gallagher, A.P. Avolio, “Determination of the ascending aortic pressure wave augmentation from the radial artery pressure pulse contour in humans,” Circulation 92 (1995) 745.
Y.Z. Yoon, M.H. Lee, K.S. Soh, “Pulse type classification by varying contact pressure,” IEEE Eng. Med. Biol. November/December (2000) 106–110.
L.S. Xu, K.Q. Wang, D. Zhang, “Modern researches on traditional Chinese pulse diagnosis,” Eur. J. Oriental Med. 4 (5) (2004) 46–54.
S.Z. Li, Pulse Diagnosis, Paradigm Press, 1985.
L. Li, Z.Z. Wang, “Study on interval variability of arterial pulse,” Proceedings of 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society BMES/EMBS Conference, vol. 1, 1999, p. 223.
J. Allen, A. Murray, “Variability of photoplethysmography peripheral pulse measurements at the ears, thumbs and toes,” A. Science, Measurement and Technology, IEE Proceedings, vol. 147, November 2000, pp. 403–407.
C.R. Meyer, H.N. Keiser, “Electrocardiogram baseline noise estimation and removal using cubic spline and state space computing techniques,” Comput. Biomed. Res. 10 (1977) 459–470.
Y. Sun, K.L. Chan, S.M. Krishnan, “ECG signal conditioning by morphological filtering,” Comput. Biol. Med. 32 (2002) 465–479. CrossRef
Sörnmo, “Time-varying filtering for removal of baseline wander in exercise ECGs,” Computers Cardiology Proceedings, 1991, pp. 145–148.
P. Strobach, K.A. Fuchs, “Event-synchronous cancellation of the heat interference in biomedical signal,” IEEE Trans. Biomed. Eng. 41 (4) (1994) 343–350. CrossRef
N.V. Thakor, Y.S. Zhu, “Application of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection,” IEEE Trans. Biomed. Eng. 38 (8) (1991) 785–794. CrossRef
L. Pablo, J. Raimon, P. Caminal, “The adaptive linear combiner with a periodic-impulse reference input as a linear comb filter,” Signal Process. 48 (3) (1996) 193–203. CrossRef
C.C. Chiu, S.J. Yeh, “A tentative approach based on Wiener filter for the reduction of respiratory effect in pulse signals,” Proceedings of 19th International Conference of IEEE/EMBS, October 1997, pp. 1394–1397.
V. Shusterman, S.I. Shah, A. Beigel, et al., “Enhancing the precision of ECG baseline correction: selective filtering and removal of residual error,” Comput. Biomed. Res. 33 (2000) 144–160. CrossRef
L.S. Xu, D. Zhang, K.Q. Wang, “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Trans. Biomed. Eng. 52 (11) (2005) 1973–1975. CrossRef
J.A. Van, T.S. Schilder, “Removal of baseline wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps,” IEEE Trans. Biomed. Eng. 32 (12) (1985) 1052–1060.
W. Huh, Y.B. Park, H.K. Kim, et al., “Development of pulse rate detection system for oriental medicine,” Proceedings of 19th International Conference-IEEE/EMBS, October 1997, pp. 2406–2408.
B.H. Wang, J.L. Xiang, “Detecting system and power spectral analysis of pulse signals of human body,” Proceedings of ICSP 1998, 1998, pp. 1646–1649.
S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Pattern Anal. Mach. Intell. 11 (7) (1989) 674–693. CrossRef
M. Unser, A. Aldroubi, “A review of wavelets in biomedical application,” Proc. IEEE 84 (1996) 626–638. CrossRef
I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, PA, 1992.
S. Conforto, T. D’Alessio, S. Pignatelli, “Optimal rejection of movement artifacts from myoelectric signals by means of a wavelet filtering procedure,” J. Electromyography Kinesiol. (1999) 47–57. CrossRef
A.J. Jerri, “The Gibbs Phenomenon in Fourier Analysis,” Splines and Wavelet Approximations, Kluwer Academic Publishers, Dordrecht, 1998. MATH
G.M. Friesen, T.C. Jannett, M.A. Jadallah, et al., “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. Biomed. Eng. 37 (1990) 85–98. CrossRef
B.U. Kohler, C. Hennig, R. Orglmeister, “The principles of software QRS detection,” IEEE Eng. Med. Biol. Mag. 21 (1) (2002) 42–57. CrossRef
M.A. Navakatikyan, C.J. Barrett, G.A. Head, J.H. Ricketts, “A real-time algorithm for the quantification of blood pressure waveforms,” IEEE Trans. Biomed. Eng. 49 (7) (2002) 662–670. CrossRef
G. Gratze, J. Fortin, A. Holler, K. Grasenick, G. Pfurtscheller, P. Wach, J. Schonegger, P. Kotanko, F. Skrabal, “A software package for noninvasive, real-time beat-to-beat monitoring of stroke volume, blood pressure, total peripheral resistance and for assessment of autonomic function,” Comput. Biol. Med. 28 (1998) 121–142.
K.G. Belani, J.J. Buckley, M.O. Poliac, “Accuracy of radial artery blood pressure determination with the vasotrac,” Canad. J. Anesthesiol. 46 (1999) 488–496. CrossRef
- Baseline Wander Correction in Pulse Waveforms Using Wavelet-Based Cascaded Adaptive Filter
- Springer Singapore
- Chapter 4
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