Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice. Comprehensive comparisons, in particular across different input representations, are scarce. We address this gap in the research landscape by a comprehensive benchmarking study covering three kinds of input representations, interpretable features, image representations and raw waveforms, across prototypical regression and classification use cases: blood pressure and atrial fibrillation prediction. In both cases, the best results are achieved by deep neural networks operating on raw time series as input representations. Within this model class, best results are achieved by modern convolutional neural networks (CNNs). but depending on the task setup, shallow CNNs are often also very competitive. We envision that these results will be insightful for researchers to guide their choice on machine learning tasks for PPG data, even beyond the use cases presented in this work.

翻译：光电容积脉搏波描记法是一种广泛使用的无创生理传感技术，适用于多种临床应用。此类临床应用正日益得到机器学习方法的支持，这引发了关于最合适的输入表示和模型选择的讨论。目前，特别是针对不同输入表示方式的全面比较研究尚显不足。我们通过一项全面的基准测试研究填补了这一研究空白，该研究涵盖了三种输入表示方式——可解释特征、图像表示和原始波形——并在典型的回归与分类应用场景（血压预测和心房颤动预测）中进行评估。在这两种应用场景中，最佳结果均通过以原始时间序列作为输入表示的深度神经网络实现。在此模型类别中，现代卷积神经网络取得了最佳性能，但根据具体任务设置，浅层CNN也往往表现出很强的竞争力。我们预计这些结果将为研究人员在选择针对PPG数据的机器学习任务方案时提供有价值的见解，甚至可延伸至本文所述应用场景之外。