Department of Digital Medicine, Brain Korea 21 Project, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea; Department of Convergence Medicine, Asan Medical Center, Seoul, 05505, Republic of Korea.
Comput Biol Med. 2024 Sep;180:108952. doi: 10.1016/j.compbiomed.2024.108952. Epub 2024 Jul 30.
Despite the growing adoption of wearable photoplethysmography (PPG) devices in personal health management, their measurement accuracy remains limited due to susceptibility to noise. This paper proposes a novel signal completion technique using generative adversarial networks that ensures both global and local consistency. Our approach innovatively addresses both short- and long-term PPG variations to restore waveforms while maintaining waveform consistency within and between pulses. We evaluated our model by removing up to 50 % of segments from segmented PPG waveforms and comparing the original and reconstructed waveforms, including systolic peak information. The results demonstrate that our method accurately reconstructs waveforms with high fidelity, producing natural and seamless transitions without discontinuities at reconstructed boundaries. Additionally, the reconstructed waveforms preserve typical PPG shapes with minimal distortion, underscoring the effectiveness and novelty of our technique.
尽管可穿戴光电容积脉搏波（PPG）设备在个人健康管理中的应用日益普及，但由于易受噪声干扰，其测量精度仍受到限制。本文提出了一种使用生成对抗网络的新型信号补全技术，可确保全局和局部一致性。我们的方法创新性地解决了 PPG 的短期和长期变化，在保持脉冲内和脉冲间的波形一致性的同时，恢复了波形。我们通过从分段 PPG 波形中去除多达 50%的片段，并比较原始和重建的波形，包括收缩峰值信息，来评估我们的模型。结果表明，我们的方法能够准确地重建具有高度逼真度的波形，在重建边界处没有不连续，产生自然而无缝的过渡。此外，重建的波形保持了典型的 PPG 形状，几乎没有失真，突出了我们技术的有效性和新颖性。
