Uncertainty quantification for machine learning models applied to photoplethysmography signals

光电容积图（PPG）信号很容易使用廉价的设备无创收集，并用于诊所和家庭监测的可穿戴设备。人们认识到，PPG信号包含大量有价值的生理信息，可用于监测或诊断一系列健康状况。机器学习（ML）应用于PPG信号，但缺乏可信度方面的工作，这在医疗环境中至关重要。通过开发量化应用于PPG信号的ML的数据和模型不确定性的方法，该项目旨在生成参考数据集来对这些模型进行基准测试，并识别具有高精度和低不确定性的模型，从而提供成熟的可信赖模型。

Photoplethysmogram (PPG) signals are easy to collect non-invasively using cheap devices and are used in the clinic and in wearable devices for home monitoring. It is recognised that PPG signals contain a wealth of valuable physiological information for monitoring or diagnosing a range of health conditions. Machine learning (ML) is applied to PPG signals but there is a lack of work on trustworthiness, which is crucial in a medical context. By developing methods to quantify both the data and model uncertainty for ML applied to PPG signals, this project aims to generate reference datasets to benchmark such models and to identify models with high accuracy and low uncertainty thus providing trustworthy models that are ripe for implementation.