Weitere Kapitel dieses Buchs durch Wischen aufrufen
This chapter aims to present a novel time series analysis approach to analyze wrist pulse signals. First, a data normalization procedure is proposed. This procedure selects a reference signal that is “closest” to a newly obtained signal from an ensemble of signals recorded from the healthy persons. Second, an auto-regressive (AR) model is constructed from the selected reference signal. Then, the residual error, which is the difference between the actual measurement for the new signal and the prediction obtained from the AR model established by reference signal, is defined as the disease-sensitive feature. This approach is based on the premise that if the signal is from a patient, the prediction model previously identified using the healthy persons would not be able to reproduce the time series measured from the patients. The applicability of this approach is demonstrated using a wrist pulse signal database collected using a Doppler ultrasound device. The classification accuracy is over 82% in distinguishing healthy persons from patients with acute appendicitis and over 90% for other diseases. These results indicate a great promise of the proposed method in telling healthy subjects from patients of specific diseases.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Shu, J., and Sun, Y., “Developing classification indices for Chinese pulse diagnosis,” Complement. Ther. Med. 15 (3)190–198, 2007. CrossRef
Hammer, L., Chinese pulse diagnosis—Contemporary approach. Eastland, Vista, 2001.
Leonard, P., Beattie, T., Addison, P., and Watson, J., “Wavelet analysis of pulse oximeter waveform permits identification of unwell children,” Emerg. Med. J. 21:59–60, 2004. CrossRef
Zhang, Y., Wang, Y., Wang, W., and Yu, J., “Wavelet feature extraction and classification of Doppler ultrasound blood flow signals,” J. Biomed. Eng. 19 (2)244–246, 2002.
Lu, W., Wang, Y., and Wang, W., “Pulse analysis of patients with severe liver problems,” IEEE Eng. Med. Biol. Mag. 18 (1)73–75, 1999. CrossRef
Zhang, A., and Yang, F., “Study on recognition of sub-health from pulse signal,” Proceedings of the ICNNB Conference. 3:1516–1518, 2005.
Zhang, D., Zhang, L., Zhang, D., and Zheng, Y., “Wavelet-based analysis of Doppler ultrasonic wrist-pulse signals,” Proceedings of the ICBBE Conference, Shanghai. 2:589–543, 2008.
Sohn, H., and Farrar, C., “Damage diagnosis using time series analysis of vibration signals,” Smart Mater. Struct. 10:446–451, 2001. CrossRef
Ljung, L., “System identification: Theory for the user. Prentice-Hall PTR, Upper Saddle River, 1999. CrossRef
Lukman, S., He, Y., and Hui, S., “Computational methods for traditional Chinese medicine: A survey,” Comput. Methods Programs Biomed. 88:283–294, 2007. CrossRef
Burges, C., A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov. 2:121–167, 1998. CrossRef
Yoon, Y., Lee, M., and Soh, K., “Pulse type classification by varying contact pressure,” IEEE Eng. Med. Biol. Mag. 19:106–110, 2000.
Powis, R., and Schwartz, R., “Practical Doppler ultrasound for the clinician,” Williams and Wilkins, Baltimore, 1991.
Wang, Y., Wu, X., Liu, B., and Yi, Y., “Definition and application of indices in Doppler ultrasound sonogram,” J. Biom. Eng. (Shanghai). 18:26–29, 1997.
Leeuwen, G., Hoeks, A., and Reneman, R., “Simulation of real- time frequency estimators for pulsed Doppler systems,” Ultrason. Imag. 8 (4)252, 1986.
Xu, L., Zhang, D., and Wang, K., “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Trans. Biomed. Eng. 52 (11)1973–1975, 2005. CrossRef
- Modified Auto-regressive Models
- Springer Singapore
- Chapter 13
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com