Biomedical Engineering, Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo São Paulo Brazil.
Physiol Meas. 2020 Aug 19;41(7):075004. doi: 10.1088/1361-6579/ab97c1.
Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed.
Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3-30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DF (HDF ) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ≤1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with [Formula: see text] Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads).
The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution.
Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.
在心房颤动（AF）中，心房主导频率（DF）图谱与非侵入性体表电位映射（BSPM）获得的图谱具有良好的空间相关性。本研究提出了一种基于小波分析的稳健 BSPM-DF 计算方法。
在每个 BSPM 信号中，使用高斯母波沿伪频率范围 3-30 Hz 进行 40 个尺度的连续小波变换。DF 是从最大能量尺度中激活时间的峰值间隔中估计的。将结果与传统广泛应用的 Welch 周期图进行比较，并在不同的协议下测试了稳健性：增加白高斯噪声水平、人为添加 DF 谐波以及减少导联数量。分析中考虑了 11 个 AF 模拟和 12 个 AF 患者。对于每个患者，在心房的 15 个位置采集心内电图。通过计算与心房 HDF （无论是模拟的还是心内测量的）的最高 DF （HDF）的绝对误差来评估两种方法的准确性，如果误差≤1 Hz，则认为是正确的。将躯干 DF 与心房 HDF 之间的误差的空间分布与心房驱动机制位置进行了比较。确定了躯干 HDF 区域（定义为 [Formula: see text] Hz 范围内的地图部分），并比较了两种方法之间的躯干占用这些区域的百分比。分析了两种方法对白高斯噪声、心室影响和谐波以及较低空间分辨率 BSPM 导联布局的稳健性：计算机 AF 模型（567 导联与 256 导联，最低至 16 导联）和患者数据（67 导联与 32 导联和 16 导联）。
该方法可提高非侵入性心房 HDF 估计的准确性。对于模型，基于小波的算法的中位数相对误差为 7.14%，而 Welch 方法为 60.00%；在患者中，误差分别为 10.03%和 12.66%。在模型（分别为 81.82%和 45.45%）和患者（分别为 66.67%和 41.67%）中，小波方法在正确估计心房 HDF 方面优于 Welch 方法。两种技术之间的正 BSPM-DF 图谱相关性较低（模型为 0.47，患者为 0.63），突出了 DF 分布的总体差异。基于小波的算法对白高斯噪声、残留心室活动和谐波更稳健，并且在空间分辨率较低的导联布局中呈现出更一致的结果。
使用 BSPM 进行心房 HDF 的估计得到了所提出的基于小波的算法的改进，有助于提高 AF 的非侵入性诊断能力。
