Department of Engineering, School of Science & Technology, Clifton Campus, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, United Kingdom; Department of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom.
Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; University Hospitals of Leicester NHS Trust, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom.
Comput Biol Med. 2019 Jan;104:299-309. doi: 10.1016/j.compbiomed.2018.11.017. Epub 2018 Nov 25.
Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from -0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to -4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.
使用体表图（BSM）对心房颤动（AF）进行非侵入性分析引起了广泛关注，人们尝试从体表信号中解释心房时频谱参数。由于这些体表信号可能受到体腔容积导体特性的影响，因此这种解释并不总是直接的。本文强调了容积导体效应以及从同时记录于心胸表面的心脏信号中识别主导频率（DF）的算法参数的影响。对 10 例持续性 AF 消融患者进行了 5 分钟的双心房虚拟电图（VEGM）和 BSM 同步记录。对 4 秒段进行了频率分析。DF 定义为在 4 到 10 Hz 之间功率最高的频率，同时应用和不应用组织指数（OI）阈值。通过分析每个 VEGM HDF 的最高 DF（HDF）与对应的 BSM 之间的差异来评估容积导体效应。可以观察到心内信号和胸内信号之间 HDF 值存在显著差异，与 OI 阈值无关。这种差异随着心内膜 HDF 的增加而增加（对于 VEGM HDF 为 6.25±0.25 Hz 的情况，BSM-VEGM 中位数差值从-0.13 Hz 增加到 9.75±0.25 Hz 的情况为-4.24 Hz），从而证实了容积导体效应在实际情况下的理论。应用 OI 阈值强烈影响 BSM HDF 区域大小和位置以及心房 HDF 区域位置。这些结果表明，必须考虑容积导体和测量算法的影响，以便进行适当的临床解释。
