Research Center for Applied Sciences , Academia Sinica , 128 Academia Rd., Sec. 2, 115-29, Nankang, Taipei City, Taiwan.
Sci Rep. 2024 Oct 10;14(1):23722. doi: 10.1038/s41598-024-75583-y.
Cuffless blood pressure (BP) measurements have long been anticipated, and the PPG (Photoplethysmography)-only method is the most promising one since already embedded in many wearable devices. To further meet the clinical accuracy requirements, PPG-only BP predictions with personalized modeling for overcoming personal deviations have been widely studied, but all required tens to hundreds of minutes of personal PPG measurements for training. Moreover, their accurate test periods without calibration practice were not reported. In this work, we collected records of PPG data from our recruited subjects in real-life scenarios instead of relying on the openly available MIMIC dataset obtained from intensive care unit (ICU) patients. Since our objective is commercial application and a substantial reduction in training data, we tailored our model training to closely mimic real-world usage. To achieve this, we developed a training approach that only requires 9-minutes of personal PPG signal recordings and mixed with other PPG data from our recruited 364 subjects. The modeling is conducted with two-channel paired inputs to the convolutional neural network (CNN)-based model, which we called Mixed Deduction Learning (MDL). The test results of 88 samples from 15 subjects, under testing period up to 30-plus days without extra calibration, revealed that MDL meets most of the standards of AAMI, BHS, and IEEE 1708-2014 (for static test only) for BP measurement devices, which indicates MDL's long-term stability and consistency. Furthermore, we found that the model with two-channel inputs presents a trend of improving performance as the pool of mixed training data increased, while the conventional one-channel input revealed degraded performance. The outperformance of MDL is attributed to many significant features remained in the first CNN layer even when mixing personal 9-minutes data with the other 364 subjects. Consequently, PPG-only with MDL introduces a new avenue for overcoming challenges in training due to personal physiological variations. Given our consideration of real-life usage, this technology can be seamlessly translated to commercial applications.
无袖带血压（BP）测量一直备受期待，而仅使用 PPG（光体积描记法）的方法是最有前途的，因为它已经嵌入到许多可穿戴设备中。为了进一步满足临床准确性要求，已经广泛研究了使用个性化建模的仅 PPG 血压预测方法，以克服个人偏差，但所有这些方法都需要数十到数百分钟的个人 PPG 测量值进行训练。此外，它们在没有校准实践的情况下进行准确测试的时间也没有报告。在这项工作中，我们从我们招募的受试者的真实场景中收集 PPG 数据记录，而不是依赖于从重症监护病房（ICU）患者获得的公开可用的 MIMIC 数据集。由于我们的目标是商业应用和大量减少训练数据，因此我们对模型训练进行了调整，使其紧密模仿现实世界的使用方式。为此，我们开发了一种训练方法，该方法仅需要 9 分钟的个人 PPG 信号记录，并与我们从 364 名受试者中招募的其他 PPG 数据混合使用。该模型使用双通道配对输入到基于卷积神经网络（CNN）的模型中进行建模，我们称之为混合推导学习（MDL）。对 15 名受试者的 88 个样本进行测试，在 30 天以上的测试期间无需额外校准，结果表明 MDL 满足 AAMI、BHS 和 IEEE 1708-2014（仅适用于静态测试）的大部分血压测量设备标准，这表明 MDL 具有长期稳定性和一致性。此外，我们发现，当混合训练数据增加时，双通道输入模型的性能呈上升趋势，而传统的单通道输入模型的性能则下降。MDL 的优异表现归因于即使将个人的 9 分钟数据与其他 364 名受试者的数据混合，仍保留在第一个 CNN 层中的许多重要特征。因此，使用 MDL 的仅 PPG 为克服由于个人生理变化而导致的训练挑战提供了新途径。鉴于我们对实际使用情况的考虑，这项技术可以无缝地转化为商业应用。
