Cardiovascular Physics and Engineering Research Group, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE7 7DN, UK.
IEEE Trans Biomed Eng. 2010 Apr;57(4):821-9. doi: 10.1109/TBME.2009.2018297. Epub 2009 Apr 7.
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.
提出了一种基于主成分分析（PCA）的 ECG 形态变化分析算法，并将其应用于从单导联 ECG 中提取替代呼吸信号。通过 PCA 描述了 ECG 特征、P 波、QRS 复合体和 T 波的呼吸诱导可变性。我们评估了哪些 ECG 特征和主成分可以为呼吸信号提供最佳的替代。20 名受试者以 4、6、8、10、12 和 14 次/分钟的频率进行 180 秒的受控呼吸和正常呼吸，并记录 ECG 和呼吸信号。通过三个算法从 ECG 中提取呼吸：基于 PCA 的算法和两个基于 RR 间隔和 QRS 幅度的现有算法。通过幅度平方相干性和互相关来比较 ECG 衍生的呼吸与记录的呼吸信号。对于相干性和相关性，排名最高的算法是应用于 QRS 复合体的 PCA 算法。与 RR 算法相比，该算法的相干性和相关性显著更高（分别为 p < 0.05 和 p < 0.0001），但与幅度算法没有显著差异。PCA 为分析不同 ECG 特征中的呼吸和非呼吸相关的逐拍变化提供了一种新的算法。
