Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt.
Sensors (Basel). 2021 Jun 22;21(13):4275. doi: 10.3390/s21134275.
The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.
12 导联心电图诞生于 100 多年前，至今仍是心脏病早期检测的重要工具。通过估计电活动随时间变化的源，可无创识别几种类型的心脏病。然而，大多数先前的研究都是基于信号处理的，因此，包括物理建模的方法将有助于解决源定位问题。本研究提出了一种通过将电分析与人体容积导体模型相结合来定位心脏源的方法，该模型既作为正向问题（forward problem），又作为反问题（inverse problem）。我们的方法不仅估计电流源的位置，还估计电流的方向。对于 12 导联心电图系统，我们评估了心脏体积、倾斜角和模型非均质性对定位的灵敏度。最后，考虑到估计的心电图源是时间序列数据，通过卡尔曼滤波对估计的源位置进行修正。在高信噪比（大于 20dB）的情况下，主要误差源是模型非均质性，这主要归因于心脏中血液的高导电性。心脏中电偶极子源的平均定位误差为 12.6mm，与先前研究的结果相当，而先前的研究考虑了不太详细的解剖结构。使用卡尔曼滤波进行时间序列源定位表明，源定位误差可以得到补偿，这表明同时使用电流方向和位置进行源估计是有效的。对于心电图 R 波，使用所提出的方法可将平均距离误差降低到 7.3mm 以下。通过考虑人体的物理特性并使用卡尔曼滤波，可以实现对心脏电信号源位置和方向的高精度估计。该方法还适用于 ECG 感应系统等电极配置。
