A principal component analysis algorithm applied to QRS complexes derived a surrogate respiratory signal with significantly higher correlation to actual breathing than the RR interval algorithm.
Observational (n=20)
No
Does a PCA-based algorithm improve the derivation of respiratory signals from single-lead ECGs compared to established RR interval and QRS amplitude algorithms in subjects performing controlled breathing?
A novel PCA-based algorithm applied to QRS complexes provides a valid surrogate for respiratory signals from single-lead ECGs, outperforming RR interval-based methods.
Absolute Event Rate: 0.81% vs 0.65%
p-value: p=<0.0001
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.
Langley et al. (Wed,) conducted a observational in Healthy subjects (n=20). Principal component analysis (PCA) algorithm applied to QRS complexes vs. RR interval algorithm was evaluated on Cross-correlation between ECG-derived respiration and recorded breathing signal (p=<0.0001). A principal component analysis algorithm applied to QRS complexes derived a surrogate respiratory signal with significantly higher correlation to actual breathing than the RR interval algorithm.