Eick Christian, Rizas Konstantinos D, Zuern Christine S, Bauer Axel
Medizinische Klinik 3, Abteilung für Kardiologie und Herzkreislauferkrankungen, Eberhard-Karls University, Tübingen, Germany.
Ann Noninvasive Electrocardiol. 2014 Mar;19(2):122-8. doi: 10.1111/anec.12107. Epub 2013 Nov 5.
Assessment of heart rate variability by means of deceleration capacity (DC) provides a noninvasive probe of cardiac autonomic activity. However, clinical use of DC is limited by the need of manual review of the ECG signals to eliminate artifacts, noise, and nonstationarities.
To validate a novel approach to fully automatically assess DC from noisy, nonstationary signals
We analyzed 100 randomly selected ECG tracings recorded for 10 minutes by routine monitor devices (GE DASH 4000, sample size 100 Hz) in a medical emergency department. We used a novel automated R-peak detection algorithm, which is mainly based on a Shannon energy envelope estimator and a Hilbert transformation. We transformed the automatically generated RR interval time series by phase-rectified signal averaging (PRSA) to assess DC of heart rate (DCauto ). DCauto was compared to DCmanual , which was obtained from the same manually preprocessed ECG signals.
DCauto and DCmanual showed good correlation and agreement, particularly if a low-pass filter was implemented into the PRSA algorithm. Correlation coefficient between DCauto and DCmanual was 0.983 (P < 0.0001). Average difference between DCauto and DCmanual was -0.23±0.49 ms with limits of agreement ranging from -1.19 to 0.73 ms. Significantly lower correlations were observed when a different R-peak detection algorithm or conventional heart rate variability (HRV) measures were tested.
DC can be fully automatically assessed from noisy, nonstationary ECG signals.
通过减速能力（DC）评估心率变异性可提供一种心脏自主神经活动的非侵入性检测方法。然而，DC的临床应用受到需要人工检查心电图信号以消除伪迹、噪声和非平稳性的限制。
验证一种从嘈杂、非平稳信号中全自动评估DC的新方法。
我们分析了在急诊科通过常规监测设备（GE DASH 4000，采样率100 Hz）随机选择记录的100份10分钟的心电图描记。我们使用了一种新型自动R波检测算法，该算法主要基于香农能量包络估计器和希尔伯特变换。我们通过相位整流信号平均（PRSA）对自动生成的RR间期时间序列进行变换，以评估心率的DC（DCauto）。将DCauto与从相同的人工预处理心电图信号获得的DCmanual进行比较。
DCauto和DCmanual显示出良好的相关性和一致性，特别是如果在PRSA算法中实施低通滤波器。DCauto与DCmanual之间的相关系数为0.983（P<0.0001）。DCauto与DCmanual之间的平均差异为-0.23±0.49 ms，一致性界限为-1.19至0.73 ms。当测试不同的R波检测算法或传统心率变异性（HRV）测量时，观察到显著较低的相关性。
可以从嘈杂、非平稳的心电图信号中全自动评估DC。
