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ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition

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Abstract

Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.

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References

  1. Folke M, Cernerud L, Ekstrm M, Hok B (2003) Critical review of non-invasive respiratory monitoring in medical care. Med Biol Eng Comput 41:377–383

    Article  PubMed  CAS  Google Scholar 

  2. de Chazal P, Heneghan C, Sheridan E, Reilly R, Nolan P, O’Malley M (2003) Automated processing of the single lead electrocardiogram for the detection of obstructive sleep apnoea. IEEE Trans Biomed Eng 50(6):686–696

    Article  PubMed  Google Scholar 

  3. Gravelyn TR, Weg JG (1980) Respiratory rate as an indicator of acute respiratory dysfunction. J Am Med Assoc 244(10):1123–1125

    Article  CAS  Google Scholar 

  4. Meredith DJ, Clifton D, Charlton P, Brooks J, Pugh CW, Tarassenko L (2012) Photoplethysmographic derivation of respiratory rate: a review of relevant physiology. J Med Eng Technol 36(1):1–7

    Article  PubMed  CAS  Google Scholar 

  5. Sharma H, Sharma KK (2016) An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions. Comput Biol Med 77:116–124

    Article  PubMed  Google Scholar 

  6. O’Brien C, Heneghan C (2007) A comparison of algorithms for estimation of a respiratory signal from the surface electrocardiogram. Comput Biol Med 37(3):305–314

    Article  PubMed  Google Scholar 

  7. Bailón R, Sőrnmo L, Laguna P (2006) ECG-derived respiratory frequency estimation. In: Clifford GD, Azuaje F, McSharry PE (eds) Advanced methods and tools for ECG data analysis. Artech House, London, pp 215–243

    Google Scholar 

  8. Chon KH, Dash S, Ju K (2009) Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Trans Biomed Eng 56(8):2054–2063

    Article  PubMed  Google Scholar 

  9. Lázaro J, Gil E, Bailón R, Mincholé A, Laguna P (2013) Deriving respiration from photoplethysmographic pulse width. Med Biol Eng Comput 51(1–2):233–242

    Article  PubMed  Google Scholar 

  10. Charlton P, Bonnici T, Tarassenko L, Alastruey J, Clifton DA, Beale R, Watkinson PJ (2017) Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiol Meas 38:669–690

    Article  PubMed  Google Scholar 

  11. Charlton PH, Bonnici T, Tarassenko L, Clifton DA, Beale R, Watkinson PJ (2016) An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol Meas 37(4):610–626

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zhang X, Ding Q (2017) Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra fusion. Biomed Signal Process Control 35:1–7

    Article  CAS  Google Scholar 

  13. Motin MA, Karmakar C, Palaniswami M (2017) Ensemble empirical mode decomposition with principal component analysis: a novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2017.2679108

    Article  PubMed  Google Scholar 

  14. Lázaro J, Alcaine A, Romero D, Gil E, Laguna P, Pueyo E, Bailón R (2014) Electrocardiogram derived respiratory rate from QRS slopes and R-wave angle. Ann Biomed Eng 42:2072–2083

    Article  PubMed  Google Scholar 

  15. Cysarz D, Zerm R, Bettermann H, Frühwirth M, Moser M, Kröz M (2008) Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow. Ann Biomed Eng 36(12):2085–2094

    Article  PubMed  Google Scholar 

  16. Schäfer A, Kratky KW (2008) Estimation of breathing rate from respiratory sinus arrhythmia: comparison of various methods. Ann Biomed Eng 36:476–485

    Article  PubMed  Google Scholar 

  17. Hahn G, Ŝipinková I, Baisch F, Hellige G (1995) Changes in the thoracic impedance distribution under different ventilatory conditions. Physiol Meas 16(3A):A161–A173

    Article  PubMed  CAS  Google Scholar 

  18. Pallas-Areny R, Colominas-Balague J, Rosell FJ (1989) The effect of respiration-induced heart movements on the ECG. IEEE Trans Biomed Eng 36(6):585–590

    Article  PubMed  CAS  Google Scholar 

  19. Orphanidou C, Fleming S, Shah SA, Tarassenko L (2013) Data fusion for estimating respiratory rate from a single-lead ECG. Biomed Signal Process Control 8(1):98–105

    Article  Google Scholar 

  20. Mirmohamadsadeghi L, Vesin JM (2014) Respiratory rate estimation from the ECG using an instantaneous frequency tracking algorithm. Biomed Signal Process Control 14:66–72

    Article  Google Scholar 

  21. Moody GB, Mark RG, Zoccola A, Mantero. S (1985) Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol 12:113–116

    Google Scholar 

  22. Pinciroli F, Rossi R, Vergani L (1985) Detection of electrical axis variation for the extraction of respiratory information. Comput Cardiol 12:499–502

    Google Scholar 

  23. Langley P, Bowers EJ, Murray A (2010) Principal component analysis as a tool for analyzing beat-to-beat changes in electrocardiogram features: application to ECG-derived respiration. IEEE Trans Biomed Eng 57(4):821–829

    Article  PubMed  Google Scholar 

  24. Widjaja D, Varon C, Dorado AC, Suykens JAK, Huffel SV (2012) Application of kernel principal component analysis for single-lead-ECG derived respiration. IEEE Trans Biomed Eng 59(4):1169–1176

    Article  PubMed  Google Scholar 

  25. Gao Y, Yan H, Xu Z, **ao M, Song J (2018) A principal component analysis based data fusion method for ECG-derived respiration from single-lead ECG. Australas Phys Eng Sci Med 41:59–67

    Article  PubMed  Google Scholar 

  26. Boyle J, Bidargaddi N, Sarela A, Karunanithi M (2009) Automatic detection of respiration rate from ambulatory single-lead ECG. IEEE Trans Inf Technol Biomed 13(6):890–896

    Article  PubMed  Google Scholar 

  27. Sharma H, Sharma KK, Bhagat OL (2015) Respiratory rate extraction from single-lead ECG using homomorphic filtering. Comput Biol Med 59:80–86

    Article  PubMed  Google Scholar 

  28. Labate D, Foresta Fl, Occhiuto G, Morabito FC, Ekuakille AL, Vergallo P (2013) Emirical mode decomposition vs. wavelet decomposition for the extraction of respiratory signal from single-channel ECG: a comparison. IEEE Sens J 13(7):2666–2674

    Article  Google Scholar 

  29. Orphanidou C (2017) Derivation of respiration rate from ambulatory ECG and PPG using ensemble empirical mode decomposition: comparison and fusion. Comput Biol Med 81:45–54

    Article  PubMed  Google Scholar 

  30. Sharma H, Sharma KK (2018) ECG-derived respiration using Hermite expansion. Biomed Sig Process Control 39:312–326

    Article  Google Scholar 

  31. Gianfelici F (2007) Multicomponent AM-FM representations: an asymptotically exact approach. IEEE Trans Audio Speech Lang Process 15(3):823–837

    Article  Google Scholar 

  32. Qin Y, Qin S, Mao Y (2008) Research on iterated Hilbert transform and its application in mechanical fault diagnosis. Mech Syst Signal Process 22(8):1967–1980

    Article  Google Scholar 

  33. Dideriksen JL, Fianfelici F, Maneski LZP, Farina D (2011) EMG-based characterization of pathological tremor using the iterated Hilbert transform. IEEE Trans Biomed Eng 58(10):2911–2921

    Article  PubMed  Google Scholar 

  34. Sharma H, Sharma KK (2016) Application of iterated Hilbert transform for deriving respiratory signal from single-lead ECG. In: 2016 1st India international conference on information processing (IICIP). https://doi.org/10.1109/IICIP.2016.7975307

  35. Iyengar N, Peng CK, Morin R, Goldberger AL, Lipsitz LA (1996) Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. Am J Physiol 271:1078–1084

    Google Scholar 

  36. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Article  PubMed  CAS  Google Scholar 

  37. Penzel T et al (2000) The apnea-ecg database. Proc Comput Cardiol 27:255–258

    Google Scholar 

  38. Portet F (2008) P wave detector with PP rhythm tracking: evaluation in different arrhythmia contexts. Physiol Meas 29(1):141–155

    Article  PubMed  Google Scholar 

  39. Sharma H, Sharma KK (2015) Baseline wander removal of ECG signals using Hilbert vibration decomposition. Electron Lett 51(6):447–449

    Article  Google Scholar 

  40. Feldman M (2006) Time-varying vibration decomposition and analysis based on the Hilbert transform. J Sound Vib 295(3–5):518–530

    Article  Google Scholar 

  41. Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32:230–236

    Article  PubMed  CAS  Google Scholar 

  42. Penzel T et al (2002) Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput 40(4):402–407

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Rourkela, India

    Hemant Sharma

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    K. K. Sharma

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  1. Hemant Sharma
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  2. K. K. Sharma
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Correspondence to Hemant Sharma.

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Sharma, H., Sharma, K.K. ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. Australas Phys Eng Sci Med 41, 429–443 (2018). https://doi.org/10.1007/s13246-018-0640-0

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