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

An algorithm for analysing 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. 20 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.