Gao Yue, Yan Hong, Xu Zhi, Xiao Meng, Song Jinzhong
China Astronauts Research and Training Center, No. 26, Beiqing Road, Haidian District, Beijing, China.
Australas Phys Eng Sci Med. 2018 Mar;41(1):59-67. doi: 10.1007/s13246-017-0612-9. Epub 2017 Dec 19.
An ECG-derived respiration (EDR) algorithm based on principal component analysis (PCA) is presented and applied to derive the respiratory signals from single-lead ECG. The respiratory-induced variabilities of ECG features, P-peak amplitude, Q-peak amplitude, R-peak amplitude, S-peak amplitude, T-peak amplitude and RR-interval, are fused by PCA to yield a better surrogate respiratory signal than other methods. The method is evaluated on data from the MIT-BIH polysomnographic database and validated against a "gold standard" respiratory obtained from simultaneously recorded respiration data. The performance of fusion algorithm is assessed by comparing the EDR signals to a reference respiratory signal, using the quantitative evaluation indexes that include true positive (TP), false positive (FP), false negative (FN), sensitivity (SE) and positive predictivity (PP). The statistically difference is significant among the PCA data fusion method and the EDR methods based on the RR intervals and the RS amplitudes, showing that PCA data fusion algorithm outperforms the others in the extraction of respiratory signals from single-lead ECGs.
提出了一种基于主成分分析（PCA）的心电图衍生呼吸（EDR）算法，并将其应用于从单导联心电图中提取呼吸信号。通过PCA融合心电图特征（P波峰值幅度、Q波峰值幅度、R波峰值幅度、S波峰值幅度、T波峰值幅度和RR间期）的呼吸诱导变异性，以产生比其他方法更好的替代呼吸信号。该方法在麻省理工学院-贝斯以色列女执事医疗中心多导睡眠图数据库的数据上进行了评估，并与从同时记录的呼吸数据中获得的“金标准”呼吸进行了验证。通过将EDR信号与参考呼吸信号进行比较，使用包括真阳性（TP）、假阳性（FP）、假阴性（FN）、敏感性（SE）和阳性预测值（PP）在内的定量评估指标，评估融合算法的性能。PCA数据融合方法与基于RR间期和RS幅度的EDR方法之间存在显著的统计学差异，表明PCA数据融合算法在从单导联心电图中提取呼吸信号方面优于其他方法。
