Advertisement

Detection of Saturation and Artifact

  • David Zhang
  • Wangmeng Zuo
  • Peng Wang
Chapter

Abstract

During the pulse signal acquisition, corruptions would be inevitably introduced such as high-frequency noise, baseline drift, saturation, and artifact. Some of the corrupted pulse signals can be recovered via preprocessing, but several types of corrupted pulse signals would be difficult to recover and should be removed from the pulse signal dataset. Therefore, low-quality pulse signal detection plays an important role in computational pulse diagnosis especially in the real-time pulse monitoring. In this work, we focus on the detection of two common pulse corruption types, i.e., saturation and artifact. For the detection of saturation, we use two criteria from its definition. For the artifact detection, we transform the pulse signal into a complex network and detect the artifact by measuring the connectivity of the network. The experimental results show that the saturation and artifact detection method can both achieve better detection accuracy and better time resolution.

References

  1. 1.
    S. Walsh and E. King, Pulse Diagnosis: A Clinical Guide. Sydney Australia: Elsevier, 2008.Google Scholar
  2. 2.
    V. D. Lad, Secrets of the Pulse. Albuquerque, New Mexico: The Ayurvedic Press, 1996.Google Scholar
  3. 3.
    E. Hsu, Pulse Diagnosis in Early Chinese Medicine. New York, American: Cambridge University Press, 2010.Google Scholar
  4. 4.
    R. Amber and B. Brooke, Pulse Diagnosis: Detailed Interpretations For Eastern & Western Holistic Treatments. Santa Fe, New Mexico: Aurora Press, 1993.Google Scholar
  5. 5.
    H. Sorvoja, V. M. Kokko, R. Myllyla, and J. Miettinen, “Use of EMFi as a blood pressure pulse transducer,” IEEE Transactions on Instrumentation and Measurement, vol. 54, pp. 2505–2512, 2005.CrossRefGoogle Scholar
  6. 6.
    E. Kaniusas, H. Pfutzner, L. Mehnen, J. Kosel, C. Tellez-Blanco, G. Varoneckas, et al., “Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG,” IEEE Sensors Journal, vol. 6, pp. 819–828, Jun 2006.CrossRefGoogle Scholar
  7. 7.
    H.-T. Wu, C.-H. Lee, and A.-B. Liu, “Assessment of endothelial function using arterial pressure signals,” Journal of Signal Processing Systems, vol. 64, pp. 223–232, 2011.CrossRefGoogle Scholar
  8. 8.
    C.-S. Hu, Y.-F. Chung, C.-C. Yeh, and C.-H. Luo, “Temporal and Spatial Properties of Arterial Pulsation Measurement Using Pressure Sensor Array,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, pp. 1–9, 2012.Google Scholar
  9. 9.
    P. Wang, W. Zuo, and D. Zhang, “A Compound Pressure Signal Acquisition System for Multichannel Wrist Pulse Signal Analysis,” Instrumentation and Measurement, IEEE Transactions on, vol. 63, pp. 1556–1565, 2014.Google Scholar
  10. 10.
    Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Wrist pulse signal diagnosis using modified Gaussian Models and Fuzzy C-Means classification,” Medical Engineering & Physics, vol. 31, pp. 1283–1289, Dec 2009.CrossRefGoogle Scholar
  11. 11.
    Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Computerized wrist pulse signal diagnosis using modified auto-regressive models,” Journal of Medical Systems, vol. 35, pp. 321–328, Jun 2011.CrossRefGoogle Scholar
  12. 12.
    L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, pp. 599–607, Jul 2012.Google Scholar
  13. 13.
    L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Classification of wrist pulse blood flow signal using time warp edit distance,” Medical Biometrics, vol. 6165, pp. 137–144, 2010.CrossRefGoogle Scholar
  14. 14.
    D. Y. Zhang, W. M. Zuo, D. Zhang, H. Z. Zhang, and N. M. Li, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” Journal of Biomedical Science and Engineering, vol. 3, pp. 361–366, 2010.CrossRefGoogle Scholar
  15. 15.
    Q. L. Guo, K. Q. Wang, D. Y. Zhang, and N. M. Li, “A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis,” in IEEE International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, China, 2008, pp. 513–517.Google Scholar
  16. 16.
    S. Charbonnier, S. Galichet, G. Mauris, and J. P. Siche, “Statistical and fuzzy models of ambulatory systolic blood pressure for hypertension diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 49, pp. 998–1003, 2000.CrossRefGoogle Scholar
  17. 17.
    H.-T. Wu, C.-H. Lee, C.-K. Sun, J.-T. Hsu, R.-M. Huang, and C.-J. Tang, “Arterial Waveforms Measured at the Wrist as Indicators of Diabetic Endothelial Dysfunction in the Elderly,” IEEE Transactions on Instrumentation and Measurement, vol. 61, pp. 162–169, 2012.CrossRefGoogle Scholar
  18. 18.
    L. Jing, S. Hao, G. Yinjing, and S. Hongyu, “Pulse Signal De-Noising Based on Integer Lifting Scheme Wavelet Transform,” in International Conference on Bioinformatics and Biomedical Engineering, WuHan, China, 2007, pp. 936–939.Google Scholar
  19. 19.
    S. Su, Q. Yan-Yan, and Q. Jun-Fei, “Research on de-noising of pulse signal based on fuzzy threshold in wavelet packet domain,” in International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2007, pp. 103–106.Google Scholar
  20. 20.
    G. Rui, W. Yiqin, Y. Jianjun, L. Fufeng, and Y. Haixia, “Wavelet based De-noising of pulse signal,” in IEEE International Symposium on IT in Medicine and Education, Xiamen, China, 2008, pp. 617–620.Google Scholar
  21. 21.
    C. Fengxiang, H. Wenxue, Z. Tao, J. Jung, and L. Xulong, “Research on Wavelet Denoising for Pulse Signal Based on Improved Wavelet Thresholding,” in International Conference on Pervasive Computing Signal Processing and Applications, Harbin, China, 2010, pp. 564–567.Google Scholar
  22. 22.
    D. Wang and D. Zhang, “Analysis of pulse waveforms preprocessing,” in International Conference onComputerized Healthcare, HongKong, 2012, pp. 175–180.Google Scholar
  23. 23.
    H. Wang, X. Wang, J. R. Deller, and J. Fu, “A Shape-Preserving Preprocessing for Human Pulse Signals Based on Adaptive Parameter Determination,” IEEE Transactions on Biomedical Circuits and Systems, vol. 8, pp. 594–604, 2013.CrossRefGoogle Scholar
  24. 24.
    G. Zheng-Gang, G. Yun, L. Li-Yan, and C. Chen, “Pulse wave signal baseline wander elimination using morphology,” in IEEE International Conference on Signal Processing Beijing, China, 2010, pp. 74–77.Google Scholar
  25. 25.
    L. Xu, D. Zhang, and K. Wang, “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering, vol. 52, pp. 1973–1975, Nov 2005.CrossRefGoogle Scholar
  26. 26.
    J. Havlik, Z. Martinovska, J. Dvorak, and L. Lhotska, “Detection of artifacts in oscillometric pulsations signals,” in Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, 2014, pp. 709–711.Google Scholar
  27. 27.
    D. Migotina, A. Calapez, and A. Rosa, “Automatic Artifacts Detection and Classification in Sleep EEG Signals Using Descriptive Statistics and Histogram Analysis: Comparison of Two Detectors,” in Engineering and Technology (S-CET), 2012 Spring Congress on, 2012, pp. 1–6.Google Scholar
  28. 28.
    L. Jinseok, D. D. McManus, S. Merchant, and K. H. Chon, “Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches,” IEEE Transactions on Biomedical Engineering, vol. 59, pp. 1499–1506, 2012.CrossRefGoogle Scholar
  29. 29.
    L. Shaopeng, H. Qingbo, R. X. Gao, and P. Freedson, “Empirical mode decomposition applied to tissue artifact removal from respiratory signal,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, 2008, pp. 3624–3627.Google Scholar
  30. 30.
    C. Dianguo, L. Liu, and W. Peng, “Removing Baseline Drift in Pulse Waveforms by a Wavelet Adaptive Filter,” in International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China, 2008, pp. 2135–2137.Google Scholar
  31. 31.
    Y. Lin, Z. Song, L. Xiaoyang, and Y. Yimin, “Removal of Pulse Waveform Baseline Drift Using Cubic Spline Interpolation,” in International Conference on Bioinformatics and Biomedical Engineering, Chengdu, China, 2010, pp. 1–3.Google Scholar
  32. 32.
    J. Zhang, X. Luo, and M. Small, “Detecting chaos in pseudoperiodic time series without embedding,” Physical Review E, vol. 73, pp. 1–5, 2006.Google Scholar
  33. 33.
    A. G. Lalkhen and A. McCluskey, “Clinical tests: sensitivity and specificity,” Continuing Education in Anaesthesia, Critical Care & Pain, vol. 8, pp. 221–223, 2008.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • David Zhang
    • 1
  • Wangmeng Zuo
    • 2
  • Peng Wang
    • 3
  1. 1.School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina
  2. 2.Harbin Institute of TechnologyHarbinChina
  3. 3.Northeast Agricultural UniversityHarbinChina

Personalised recommendations