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Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration

Lookup NU author(s): Dr Philip Langley, Emma Bowers, Professor Alan MurrayORCiD



Analgorithm 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 non-respiratory related beat-to-beat changes in different ECG features.

Publication metadata

Author(s): Langley P, Bowers EJ, Murray A

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Biomedical Engineering

Year: 2010

Volume: 57

Issue: 4

Pages: 821-829

Print publication date: 07/04/2010

Date deposited: 21/05/2010

ISSN (print): 0018-9294

ISSN (electronic): 1558-2531

Publisher: IEEE


DOI: 10.1109/TBME.2009.2018297


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