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Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition

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Abstract

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal’s amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects’ RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm.

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Abbreviations

AB:

Abdominal breathing

AM:

Abdominal muscles

AWM:

Abdominal wall movement

BMI:

Body mass index

CEEMD:

Complementary ensemble empirical mode decomposition

DM:

Diaphragm

EMG:

Electromyography

IMF:

Intrinsic mode function

MMG:

Mechanomyography

RIP:

Respiratory inductance plethysmography

TB:

Thoracic breathing

TAM:

Thoracoabdominal movement

TM:

Thoracic muscles

TWM:

Thoracic wall movement

References

  1. Alves N, Chau T (2010) Uncovering patterns of forearm muscle activity using multi-channel mechanomyography. J Electromyogr Kinesiol 20(5):777–786. https://doi.org/10.1016/j.jelekin.2009.09.003

    Article  PubMed  Google Scholar 

  2. Aoude AA, Kearney RE, Brown KA, Galiana HL, Robles-Rubio CA (2011) Automated off-line respiratory event detection for the study of postoperative apnea in infants. IEEE Trans Biomed Eng 58(6):1724–1733. https://doi.org/10.1109/tbme.2011.2112657

    Article  PubMed  Google Scholar 

  3. Ayoub J, Cohendy R, Prioux J, Ahmaidi S, Bourgeois JM, Dauzat M, Ramonatxo M, Préfaut C (2001) Diaphragm movement before and after cholecystectomy: a sonographic study. Anesth Analg 92(3):755–761. https://doi.org/10.1213/00000539-200103000-00038

    Article  PubMed  CAS  Google Scholar 

  4. Chang C-C, Kao S-C, Hsiao T-C, Hsu H-Y (2014) Assessment of autonomic nervous system by using empirical mode decomposition-based reflection wave analysis during non-stationary conditions. Physiol Meas 35(9):1873–1883. https://doi.org/10.1088/0967-3334/35/9/1873

    Article  PubMed  CAS  Google Scholar 

  5. Chen Y-C, Hsiao T-C (2016) Instantaneous phase difference analysis between thoracic and abdominal movement signals based on complementary ensemble empirical mode decomposition. Biomed Eng Online 15(1):1–21. https://doi.org/10.1186/s12938-016-0233-7

    Article  Google Scholar 

  6. Chen Y-C, Hsiao T-C, Hsu J-H, Chen J-L (2014) Breathing pattern recognition of abdominal wall movement by using ensemble empirical mode decomposition. Adv Adapt Data Anal 6(01):1450002–1450018. https://doi.org/10.1142/s1793536914500022

    Article  Google Scholar 

  7. Chen Y-C, Hsiao T-C, Chen J-L (2015) Better thoracoabdominal synchrony in abdominal breathing: evidence from complementary ensemble empirical mode decomposition-based Lissajous figure analysis. J Med Imaging Health Inf 5(2):400–405. https://doi.org/10.1166/jmihi.2015.1406

    Article  Google Scholar 

  8. Cluzel P, Similowski T, Chartrand-Lefebvre C, Zelter M, Derenne J-P, Grenier PA (2000) Diaphragm and chest wall: assessment of the inspiratory pump with MR imaging—preliminary observations. Radiology 215(2):574–583. https://doi.org/10.1148/radiology.215.2.r00ma28574

    Article  PubMed  CAS  Google Scholar 

  9. Duiverman ML, van Eykern LA, Vennik PW, Koëter GH, Maarsingh EJ, Wijkstra PJ (2004) Reproducibility and responsiveness of a noninvasive EMG technique of the respiratory muscles in COPD patients and in healthy subjects. J Appl Physiol 96(5):1723–1729. https://doi.org/10.1152/japplphysiol.00914.2003

    Article  PubMed  Google Scholar 

  10. Estrada L, Torres A, Sarlabous L, Fiz JA, Jané R (2014) Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference, pp 3204–3207

  11. Finney DJ (1952) Statistical method in biological assay. Griffin, London

    Google Scholar 

  12. Ganong WF (2005) Review of medical physiology, 22nd edn. Mcgraw-Hill, California

    Google Scholar 

  13. Guo J-Y, Zheng Y-P, Xie H-B, Chen X (2010) Continuous monitoring of electromyography (EMG), mechanomyography (MMG), sonomyography (SMG) and torque output during ramp and step isometric contractions. Med Eng Phys 32(9):1032–1042. https://doi.org/10.1016/j.medengphy.2010.07.004

    Article  PubMed  Google Scholar 

  14. Hammer J, Newth CJL (2009) Assessment of thoraco-abdominal asynchrony. Paediatr Respir Rev 10(2):75–80. https://doi.org/10.1016/j.prrv.2009.02.004

    Article  PubMed  CAS  Google Scholar 

  15. Hogrel J-Y (2005) Clinical applications of surface electromyography in neuromuscular disorders. Neurophysiol Clin 35(2-3):59–71. https://doi.org/10.1016/j.neucli.2005.03.001

    Article  PubMed  Google Scholar 

  16. Hu X, Tong K, Li L (2007) The mechanomyography of persons after stroke during isometric voluntary contractions. J Electromyogr Kinesiol 17(4):473–483. https://doi.org/10.1016/j.jelekin.2006.01.015

    Article  PubMed  CAS  Google Scholar 

  17. Huang NE, Shen Z, Long SR, MC W, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  Google Scholar 

  18. Islam MA, Sundaraj K, Ahmad RB, Ahamed NU (2013) Mechanomyogram for muscle function assessment: a review. PLoS One 8(3):e58902. https://doi.org/10.1371/journal.pone.0058902

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Issa FG, Sullivan CE (1985) Respiratory muscle activity and thoracoabdominal motion during acute episodes of asthma during sleep. Am Rev Respir Dis 132(5):999–1004. https://doi.org/10.1164/arrd.1985.132.5.999

    Article  PubMed  CAS  Google Scholar 

  20. Kolar P, Sulc J, Kyncl M, Sanda J, Cakrt O, Andel R, Kumagai K, Kobesova A (2012) Postural function of the diaphragm in persons with and without chronic low back pain. J Orthop Sports Phys Ther 42(4):352–362. https://doi.org/10.2519/jospt.2012.3830

    Article  PubMed  Google Scholar 

  21. Leis JW (2011) Digital signal processing using MATLAB for students and researchers. John Wiley & Sons, New Jersey. https://doi.org/10.1002/9781118033623

    Book  Google Scholar 

  22. Lourens MS, van den Berg B, Aerts JG, Verbraak AF, Hoogsteden HC, Bogaard JM (2000) Expiratory time constants in mechanically ventilated patients with and without COPD. Intensive Care Med 26(11):1612–1618. https://doi.org/10.1007/s001340000632

    Article  PubMed  CAS  Google Scholar 

  23. Mayer OH, Clayton RG, Jawad AF, McDonough JM, Allen JL (2003) Respiratory inductance plethysmography in healthy 3-to 5-year-old children. Chest 124(5):1812–1819. https://doi.org/10.1378/chest.124.5.1812

    Article  PubMed  Google Scholar 

  24. Melendez JA, Alagesan R, Reinsel R, Weissman C, Burt M (1992) Postthoracotomy respiratory muscle mechanics during incentive spirometry using respiratory inductance plethysmography. Chest 101(2):432–436. https://doi.org/10.1378/chest.101.2.432

    Article  PubMed  CAS  Google Scholar 

  25. Musante G, Schulze A, Gerhardt T, Everett R, Claure N, Schaller P, Bancalari E (2001) Proportional assist ventilation decreases thoracoabdominal asynchrony and chest wall distortion in preterm infants. Pediatr Res 49(2):175–180. https://doi.org/10.1203/00006450-200102000-00008

    Article  PubMed  CAS  Google Scholar 

  26. Newth C, Hammer J (2005) Measurements of thoraco-abdominal asynchrony and work of breathing in children. Prog Respir Res 33:148–156

    Article  Google Scholar 

  27. Ogawa Y, Iwasaki K, Shibata S, Kato J, Ogawa S, Oi Y (2006) Different effects on circulatory control during volatile induction and maintenance of anesthesia and total intravenous anesthesia: autonomic nervous activity and arterial cardiac baroreflex function evaluated by blood pressure and heart rate variability analysis. J Clin Anesth 18(2):87–95. https://doi.org/10.1016/j.jclinane.2005.06.004

    Article  PubMed  Google Scholar 

  28. Rees K, Kingshott RN, Wraith PK, Douglas NJ (2000) Frequency and significance of increased upper airway resistance during sleep. Am J Respir Crit Care Med 162(4):1210–1214. https://doi.org/10.1164/ajrccm.162.4.9908052

    Article  PubMed  CAS  Google Scholar 

  29. Reyes B, Reljin N, Kong Y, Nam Y, Chon K (2016) Tidal volume and instantaneous respiration rate estimation using a smartphone camera. IEEE J Biomed Health Inform 21(3):764–777. https://doi.org/10.1109/jbhi.2016.2532876

    Article  PubMed  Google Scholar 

  30. Rilling G, Flandrin P, Goncalves P (2003) On empirical mode decomposition and its algorithms. In: IEEE-EURASIP workshop on nonlinear signal and image processing, Grado, Italy

  31. Sarlabous L, Torres A, Fiz JA, Gea J, Martínez-Llorens JM, Jané R (2009) Evaluation of the respiratory muscular function by means of diaphragmatic mechanomyographic signals in COPD. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference, Minneapolis, USA

  32. Sarlabous L, Torres A, Fiz JA, Morera J, Jané R (2013) Index for estimation of muscle force from mechanomyography based on the Lempel–Ziv algorithm. J Electromyogr Kinesiol 23(3):548–557. https://doi.org/10.1016/j.jelekin.2012.12.007

    Article  PubMed  Google Scholar 

  33. Sarlabous L, Torres A, Fiz JA, Jané R (2014) Evidence towards improved estimation of respiratory muscle effort from diaphragm mechanomyographic signals with cardiac vibration interference using sample entropy with fixed tolerance values. PLoS One 9(2):e88902. https://doi.org/10.1371/journal.pone.0088902

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Shannon CE (1949) Communication in the presence of noise. Proc IRE 37(1):10–21. https://doi.org/10.1109/JRPROC.1949.232969

    Article  Google Scholar 

  35. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55. https://doi.org/10.1145/584091.584093

    Article  Google Scholar 

  36. Sherwood L (2015) Human physiology: from cells to systems. Cengage learning, Boston

    Google Scholar 

  37. Siafakas NM, Mitrouska I, Bouros D, Georgopoulos D (1999) Surgery and the respiratory muscles. Thorax 54(5):458–465. https://doi.org/10.1136/thx.54.5.458

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Stewart H, Eisen A, Road J, Mezei M, Weber M (2001) Electromyography of respiratory muscles in amyotrophic lateral sclerosis. J Neurol Sci 191(1-2):67–73. https://doi.org/10.1016/S0022-510X(01)00621-9

    Article  PubMed  CAS  Google Scholar 

  39. Stokes M, Blythe G (2001) Muscle sounds in physiology, sports science and clinical investigation: applications and history of mechanomyography. Medical Intelligence, Oxford

    Google Scholar 

  40. Torres A, Fiz JA, Galdiz JB, Gea J, Morera J, Jane R (2006) Inspiratory pressure evaluation by means of the entropy of respiratory mechanomyographic signals. In: 28th Annual International Conference of the IEE Engineering in Medicine and Biology Society, pp 5735–5738

  41. Torres A, Fiz JA, Jané R, Galdiz JB, Gea J, Morera J (2007) Application of the empirical mode decomposition method to the analysis of respiratory mechanomyographic signals. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, pp 1566–1569

  42. Watt JH, Van den Berg SA (1995) Research methods for communication science. Allyn & Bacon, USA

    Google Scholar 

  43. West JB (2012) Respiratory physiology: the essentials, 9th edn. Lippincott Williams & Wilkins, USA

    Google Scholar 

  44. Westerdahl E, Lindmark B, Eriksson T, Friberg O, Hedenstierna G, Tenling A (2005) Deep breathing exercises reduce atelectasis and improve pulmonary function after coronary artery bypass surgery. Chest 128(5):3482–3488. https://doi.org/10.1378/chest.128.5.3482

    Article  PubMed  Google Scholar 

  45. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41. https://doi.org/10.1142/S1793536909000047

    Article  Google Scholar 

  46. Yeh J-R, Shieh J-S, Huang NE (2010) Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv Adapt Data Anal 2(02):135–156. https://doi.org/10.1142/S1793536910000422

    Article  Google Scholar 

  47. Zwarts MJ, Drost G, Stegeman DF (2000) Recent progress in the diagnostic use of surface EMG for neurological diseases. J Electromyogr Kinesiol 10(5):287–291. https://doi.org/10.1016/S1050-6411(00)00020-1

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

N. E. Huang (an academician of Academia Sinica and a member of National Academy of Engineering) is gratefully acknowledged for the empirical mode decomposition studies.

Funding

This work was fully supported by the Taiwan Ministry of Science and Technology under grant numbers MOST 103-2221-E-009-139 and MOST 105-2221-E-009-159. This work was also supported in part by the “Aim for the Top University Plan” of Biomedical Electronics Translational Research Center in National Chiao Tung University and Ministry of Education, Taiwan, R.O.C.

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Authors and Affiliations

  1. Institute of Computer Science and Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Ya-Chen Chen

  2. Institute of Biomedical Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Tzu-Chien Hsiao

  3. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Tzu-Chien Hsiao

  4. Biomedical Electronics Translational Research Center and Biomimetic Systems Research Center, National Chiao Tung University, Hsinchu, Taiwan, Republic of China

    Tzu-Chien Hsiao

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  1. Ya-Chen Chen
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Correspondence to Tzu-Chien Hsiao.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. The current study was under the research project “Paced-respiratory induced heart rate variability and cardiac output evaluation (Protocol No: 100-015-E),” which was approved by the Institution Review Board of the National Taiwan University Hospital Hsinchu Branch. The committee is organized under and operates in accordance with the Good Clinical Practice guidelines and governmental laws and regulations. The experiment and the use of the data obtained from the human subject were performed in accordance with the approved protocol.

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Chen, YC., Hsiao, TC. Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition. Med Biol Eng Comput 56, 1293–1303 (2018). https://doi.org/10.1007/s11517-017-1766-z

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  • DOI: https://doi.org/10.1007/s11517-017-1766-z

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