Uncertainty quantification for machine learning models applied to photoplethysmography signals - 22HLT01 QUMPHY

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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.