CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution.
CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution.
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CVAR-seg：用于传导速度和振幅恢复的自动信号分割管道。
DOI：
10.3389/fphys.2021.673047
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发表时间：
2021
影响因子：
4
通讯作者：
Loewe A
中科院分区：
文献类型：
--
作者：
Nothstein M;Luik A;Jadidi A;Sánchez J;Unger LA;Wülfers EM;Dössel O;Seemann G;Schmitt C;Loewe A
速率变化S1 S2刺激方案可用于恢复研究，以表征心房基质、离子重构和房颤风险。对众多患者的临床恢复研究产生了大量的这些数据。因此，用于评估临床采集的S1 S2刺激协议数据的自动化流水线需要对局部激动时间、电描记图振幅和传导速度进行一致、稳健、可再现和精确的评估。在这里，我们提出了CVAR-Seg流水线，开发重点是三个挑战：（i）没有刺激参数的先前知识，因此，支持任意协议。(ii)该管道在不同的噪声条件下保持稳健。(iii)该流水线支持在时间上接近刺激伪影的心房活动的分割，这是具有挑战性的，因为与心房活动相比，刺激的幅度和斜率更大。通过时间间隔检测估计S1基本周期长度。通过检测不同通道中超过幅度阈值的同步峰并识别检测到的刺激之间的时间间隔来分割刺激时间窗。通过匹配滤波器消除刺激伪影允许检测时间接近的局部激活时间。非线性信号能量算子用于分割心房活动周期。测地线和欧几里德电极间距离允许近似的传导速度。CVAR-Seg流水线的自动分割性能在信噪比降低的37个合成数据集上进行了评估。通过重构临床噪声的频谱，对噪声进行建模。对于低至0 dB的信噪比，管道保留了低于单个样本（1 ms）的中值局部激活时间误差，代表高临床噪声水平。作为概念验证，在阵发性房颤患者的CARTO病例上对管道进行了测试，并得出了传导速度和振幅的合理恢复曲线。所提出的公开可用的CVAR-Seg管道承诺即使在低信噪比的情况下也能快速、全自动、鲁棒和准确地评估心房信号。这是通过解决刺激和心房活动的邻近问题来实现的，以实现标准化评估，而不会对大型数据集引入人为偏见。
Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.
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影响因子：
--
作者：
Lenis G;Pilia N;Loewe A;Schulze WH;Dössel O
通讯作者：
Dössel O
DOI：
10.1016/s0735-1097(97)00385-9
发表时间：
1997-12-01
影响因子：
24
作者：
Franz, MR;Karasik, PL;Chavez, M
通讯作者：
Chavez, M
影响因子：
24
作者：
Jadidi, Amir S.;Cochet, Hubert;Jais, Pierre
通讯作者：
Jais, Pierre
影响因子：
7.7
作者：
Abdi, Bahareh;Hendriks, Richard C.;de Groot, Natasja M. s
通讯作者：
de Groot, Natasja M. s
影响因子：
2.7
作者：
Franz, MR
通讯作者：
Franz, MR