Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration.
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.
Cardiovascular Physics and Engineering Research Group, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE7 7DN, UK. philip.langley@ncl.ac.uk
Philip
P
Langley
Emma J
EJ
Bowers
Alan
A
Murray
D000328:Adult; D000465:Algorithms; D018880:Atrial Premature Complexes / Q000503:physiopathology; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D056152:Respiratory Rate / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric