Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

Article, Other literature type English OPEN
Fu-Tai Wang; Hsiao-Lung Chan; Chun-Li Wang; Hung-Ming Jian; Sheng-Hsiung Lin;

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the n... View more
  • References (21)
    21 references, page 1 of 3

    1. Ruehland, W.R.; Rochford, P.D.; O'Donoghue, F.J.; Pierce, R.J.; Singh, P.; Thornton, A.T. The new Aasm criteria for scoring hypopneas: Impact on the apnea hypopnea index. Sleep 2009, 32, 150-157.

    2. Brack, T.; Thűer, I.; Clarenbach, C.F.; Senn, O.; Noll, G.; Russi, E.W.; Bloch, K.E. Daytime Cheyne-Stokes respiration in ambulatory patients with severe congestive heart failure is associated with increased mortality. Chest 2007, 132, 1463-1471.

    3. Lanfranchi, P.A.; Braghiroli, A.; Bosimini, E.; Mazzuero, G.; Colombo, R.; Donner, C.F.; Giannuzzi, P. Prognostic value of nocturnal Cheyne-Stokes respiration in chronic heart failure. Circulation 1999, 99, 1435-1440.

    4. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H. The empirical decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 1998, 454, 903-995.

    5. Srhoj-Egekher, V.; Cifrek, M.; Medved, V. The application of Hilbert-Huang transform in the analysis of muscle fatigue during cyclic dynamic contractions. Med. Biol. Eng. Comput. 2011, 49, 659-669.

    6. Yeh, J.R.; Sun, W.Z.; Wen, Y.R.; Shieh, J.S. A novel continuous visual analog scale model derived from pain-relief demand index via Hilbert Huang transform for postoperative pain. J. Med. Biol. Eng. 2011, 31, 169-176.

    7. Yeh, J.R.; Peng, C.K.; Lo, M.T.; Yeh, C.H.; Chen, S.C.; Wang, C.Y.; Lee, P.L.; Kang, J.H. Investigating the interaction between heart rate variability and sleep EEG using nonlinear algorithms. J. Neurosci. Methods 2013, 219, 233-239.

    8. Blanco-Velasco, M.; Weng, B.; Barner, K.E. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 2008, 38, 1-13.

    9. Chang, K.M. Arrhythmia ECG noise reduction by ensemble empirical mode decomposition. Sensors 2010, 10, 6063-6080.

    10. Janušauskas, A.; Marozas, V.; Lukoševičius, A. Ensemble empirical mode decomposition based feature enhancement of cardio signals. Med. Eng. Phys. 2013, 35, 1059-1069.

  • Related Organizations (3)
  • Metrics
Share - Bookmark