Tang Qunfeng, Chen Zhencheng, Guo Yanke, Liang Yongbo, Ward Rabab, Menon Carlo, Elgendi Mohamed
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Front Physiol. 2022 Apr 25;13:859763. doi: 10.3389/fphys.2022.859763. eCollection 2022.
Electrocardiography and photoplethysmography are non-invasive techniques that measure signals from the cardiovascular system. While the cycles of the two measurements are highly correlated, the correlation between the waveforms has rarely been studied. Measuring the photoplethysmogram (PPG) is much easier and more convenient than the electrocardiogram (ECG). Recent research has shown that PPG can be used to reconstruct the ECG, indicating that practitioners can gain a deep understanding of the patients' cardiovascular health using two physiological signals (PPG and ECG) while measuring only PPG. This study proposes a subject-based deep learning model that reconstructs an ECG using a PPG and is based on the bidirectional long short-term memory model. Because the ECG waveform may vary from subject to subject, this model is subject-specific. The model was tested using 100 records from the MIMIC III database. Of these records, 50 had a circulatory disease. The results show that a long ECG signal could be effectively reconstructed from PPG, which is, to our knowledge, the first attempt in this field. A length of 228 s of ECG was constructed by the model, which was trained and validated using 60 s of PPG and ECG signals. To segment the data, a different approach that segments the data into short time segments of equal length (and that do not rely on beats and beat detection) was investigated. Segmenting the PPG and ECG time series data into equal segments of 1-min width gave the optimal results. This resulted in a high Pearson's correlation coefficient between the reconstructed 228 s of ECG and referenced ECG of 0.818, while the root mean square error was only 0.083 mV, and the dynamic time warping distance was 2.12 mV per second on average.
心电图和光电容积脉搏波描记法是测量心血管系统信号的非侵入性技术。虽然这两种测量的周期高度相关，但波形之间的相关性很少被研究。测量光电容积脉搏波描记图（PPG）比心电图（ECG）更容易、更方便。最近的研究表明，PPG可用于重建ECG，这表明从业者在仅测量PPG时，利用两种生理信号（PPG和ECG）就能深入了解患者的心血管健康状况。本研究提出了一种基于双向长短期记忆模型的、利用PPG重建ECG的基于个体的深度学习模型。由于ECG波形可能因个体而异，该模型是针对个体的。使用多参数智能监测数据库III（MIMIC III）中的100条记录对该模型进行了测试。在这些记录中，50条患有循环系统疾病。结果表明，可以从PPG有效地重建长ECG信号，据我们所知，这是该领域的首次尝试。该模型构建了228秒的ECG，使用60秒的PPG和ECG信号对其进行训练和验证。为了对数据进行分段，研究了一种不同的方法，即将数据分割成等长的短时间段（且不依赖于心跳和心跳检测）。将PPG和ECG时间序列数据分割成1分钟宽度的等长段可得到最佳结果。这使得重建的228秒ECG与参考ECG之间的皮尔逊相关系数高达0.818，而均方根误差仅为0.083 mV，动态时间规整距离平均为每秒2.12 mV。
