ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

Abstract

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.

Sensitivity of Electrocardiogram on Electrode-Pair Locations for Wearable Devices: Computational Analysis of Amplitude and Waveform Distortion

“…ECG was performed using the following procedure based on our previous studies [ 21 , 22 ]: The surface potential of the anatomical human body model was calculated using the SPFD method with a single electric dipole as the input source. Next, the propagation time of the cardiac action potential was calculated using the propagation velocity and the distance between successive charges.…”

“…where ϕ n , l mean , and l dn indicate the scalar potential calculated at each dipole, the average dipole length of each part, and the dipole lengths from each analysis, respectively. ECG was performed using the following procedure based on our previous studies [21,22]: The surface potential of the anatomical human body model was calculated using the SPFD method with a single electric dipole as the input source. Next, the propagation time of the cardiac action potential was calculated using the propagation velocity and the distance between successive charges.…”

“…The value of d at one electrode is calculated from the average of the current paths from (4) to (8). The mechanism of generating the myocardial action potential was simulated by sequentially placing single electric dipoles [22] in the stimulated conduction system of the heart, as validated previously [21,22]. The length of each electric dipole was 2 mm, and the dominant frequency component of the myocardial action potential was 1 Hz.…”

“…The implementation of the tetrahedral mesh makes it difficult to fully account for the morphological differences between individuals. We previously developed a computational method to replicate the ECG signal by combining the scalar-potential finite-difference (SPFD) method and time-domain signal processing with validation [ 21 , 22 ]. The proposed method, which can use a voxelized human model, enables the evaluation of ECG signals considering interindividual morphological differences.…”