He Shijie, Dong Hanrui, Zhang Xianbin, Millham Richard, Xu Lin, Wu Wanqing
School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Department of Information Technology, Durban University of Technology, Durban 4001, South Africa.
Sensors (Basel). 2025 Mar 14;25(6):1810. doi: 10.3390/s25061810.
The rapid development of smart wearable devices has significantly advanced noninvasive, continuous health monitoring, enabling real-time collection of vital biosignals. Electrocardiographic imaging (ECGI), a noninvasive technique that reconstructs transmembrane potential (TMP) from body surface potential, has emerged as a promising method for reflecting cardiac electrical activity. However, the ECG inverse problem's inherent instability has hindered its practical application. To address this, we introduce a novel Priori-to-Attention Network (P2AN) that enhances the stability of ECGI solutions. By leveraging the one-dimensional nature of electrical signals and the body's electrical propagation properties, P2AN uses small-scale convolutions for attention computation, integrating a priori physiological knowledge via cross-attention mechanisms. This approach eliminates the need for clinical TMP measurements and improves solution accuracy through normalization constraints. We evaluate the method's effectiveness in diagnosing myocardial ischemia and ventricular hypertrophy, demonstrating significant improvements in TMP reconstruction and lesion localization. Moreover, P2AN exhibits high robustness in noisy environments, making it highly suitable for integration with wearable electrocardiographic clothing. By improving spatiotemporal accuracy and noise resilience, P2AN offers a promising solution for noninvasive, real-time cardiovascular monitoring using AI-powered wearable devices.
智能可穿戴设备的迅速发展极大地推动了无创、连续的健康监测，实现了重要生物信号的实时采集。心电图成像（ECGI）是一种从体表电位重建跨膜电位（TMP）的无创技术，已成为反映心脏电活动的一种有前景的方法。然而，心电图逆问题固有的不稳定性阻碍了其实际应用。为了解决这一问题，我们引入了一种新颖的先验到注意力网络（P2AN），以增强ECGI解决方案的稳定性。通过利用电信号的一维特性和人体的电传播特性，P2AN使用小规模卷积进行注意力计算，通过交叉注意力机制整合先验生理知识。这种方法无需临床TMP测量，并通过归一化约束提高了解决方案的准确性。我们评估了该方法在诊断心肌缺血和心室肥大方面的有效性，证明了在TMP重建和病变定位方面有显著改善。此外，P2AN在嘈杂环境中表现出高鲁棒性，使其非常适合与可穿戴心电图服装集成。通过提高时空准确性和抗噪声能力，P2AN为使用人工智能驱动的可穿戴设备进行无创、实时心血管监测提供了一个有前景的解决方案。
