Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration

Langley, Philip; Bowers, Emma J.; Murray, Alan

Abstract

An algorithm for analyzing changes in ECG morphology based on principal component analysis (PCA) is presented and applied to the derivation of surrogate respiratory signals from single-lead ECGs. The respiratory-induced variability of ECG features, P waves, QRS complexes, and T waves are described by the PCA. We assessed which ECG features and which principal components yielded the best surrogate for the respiratory signal. Twenty subjects performed controlled breathing for 180 s at 4, 6, 8, 10, 12, and 14 breaths per minute and normal breathing. ECG and breathing signals were recorded. Respiration was derived from the ECG by three algorithms: the PCA-based algorithm and two established algorithms, based on RR intervals and QRS amplitudes. ECG-derived respiration was compared to the recorded breathing signal by magnitude squared coherence and cross-correlation. The top ranking algorithm for both coherence and correlation was the PCA algorithm applied to QRS complexes. Coherence and correlation were significantly larger for this algorithm than the RR algorithm(p < 0.05 and p < 0.0001, respectively) but were not significantly different from the amplitude algorithm. PCA provides a novel algorithm for analysis of both respiratory and nonrespiratory related beat-to-beat changes in different ECG features.

Citation

Langley, P., Bowers, E. J., & Murray, A. (2010). Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration. IEEE transactions on bio-medical engineering / Bio-medical Engineering Group, 57(4), 821-829. https://doi.org/10.1109/TBME.2009.2018297

• Journal Article Type: Article
• Online Publication Date: Apr 7, 2009
• Publication Date: 2010-04
• Deposit Date: Nov 13, 2014
• Journal: Ieee Transactions On Biomedical Engineering
• Print ISSN: 0018-9294
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 4
• Pages: 821-829
• DOI: https://doi.org/10.1109/TBME.2009.2018297
• Keywords: Biomedical Engineering
• Public URL: https://hull-repository.worktribe.com/output/368808
• Publisher URL: http://ieeexplore.ieee.org/document/4811954/
• Contract Date: Nov 13, 2014