对比三种输入方式，发现原始波形+深度卷积网络最适合心率相关预测。
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
- 用原始波形、图像和可解释特征三类输入做对比实验
- 血压和房颤预测中，深度卷积网络在原始波形上表现最佳
- 浅层CNN也表现不俗，适合资源受限场景
光电容积脉搏波（PPG）是一种广泛应用的无创生理传感技术，适用于多种临床场景。随着机器学习方法在该领域的深入应用，关于最优输入表示和模型选择的问题日益突出。然而，针对不同输入表示的全面比较仍较为稀缺。本文通过一项综合性基准研究，覆盖三类输入表示：可解释特征、图像表示和原始波形，针对典型的回归与分类任务——血压预测与房颤检测进行评估。结果表明，在两种任务中，基于原始时间序列输入的深度神经网络均取得最佳性能；其中，现代卷积神经网络（CNN）表现最优，但在特定任务设置下，浅层CNN同样具有竞争力。本研究为研究人员在处理PPG数据时的建模选择提供了实用指导，其结论可推广至其他相关任务。
原文摘要 · Abstract (English)
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.
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