ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition
- 104 Downloads
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.
KeywordsECG Respiration ECG-derived respiration IHT HVD
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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 215–243Google 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 PubMedGoogle Scholar
- 21.Moody GB, Mark RG, Zoccola A, Mantero. S (1985) Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol 12:113–116Google Scholar
- 22.Pinciroli F, Rossi R, Vergani L (1985) Detection of electrical axis variation for the extraction of respiratory information. Comput Cardiol 12:499–502Google 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 271:1078–1084Google Scholar
- 37.Penzel T et al (2000) The apnea-ecg database. Proc Comput Cardiol 27:255–258Google Scholar