论文标题
Cardiogan：带有双歧视因子的细心生成对抗网络，用于合成PPG的ECG
CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG
论文作者
论文摘要
心电图（ECG）是心脏活性的电测量，而光心理图（PPG）是血液循环体积变化的光学测量。尽管这两个信号都用于心率监测，但从医学角度来看，ECG更有用，因为它带有其他心脏信息。尽管在智能手表或类似的可穿戴设备中进行了许多尝试将ECG传感纳入连续和可靠的心脏监测设备，但PPG传感器是可用的主要可行传感解决方案。为了解决这个问题，我们提出了Cardiogan，这是一种对抗模型，将PPG作为输入并生成ECG作为输出。提出的网络利用基于注意力的生成器来学习本地显着特征，以及双重歧视器来保留在时间和频域中生成的数据的完整性。我们的实验表明，与原始输入PPG相比，Cardiogan生成的ECG提供了更可靠的心率测量值，从而将误差从每分钟的9.74 BEATS（从PPG测量）减少到2.89（从生成的ECG测量）。
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. The proposed network utilizes an attention-based generator to learn local salient features, as well as dual discriminators to preserve the integrity of generated data in both time and frequency domains. Our experiments show that the ECG generated by CardioGAN provides more reliable heart rate measurements compared to the original input PPG, reducing the error from 9.74 beats per minute (measured from the PPG) to 2.89 (measured from the generated ECG).
