Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable

In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable. Although existing photoplethysmography (PPG) heart rate sensors provide reliable measurements, they use considerably more energy than accelerometers and have a major impact on battery life of wearable devices. By using energy-efficient accelerometers to predict heart rate, significant energy savings can be made. Further, we are interested in understanding patient recovery after a heart rate intervention, where we expect a variation in heart rate over time. Therefore, we propose an online approach to tackle the concept as time passes. We evaluate the methods on approximately 4 weeks of free living data from three patients over a number of months. We show that our approach can achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).Comment: MLMH 2018: 2018 KDD Workshop on Machine Learning for Medicine and Healthcar

Computational Modelling in the Management of Patients with Aortic Valve Stenosis

Background Stenosis of the aortic valve causes increased left ventricular pressure leading to adverse clinical outcomes. The selection and timing of intervention (surgical replacement or transcatheter implantation) is often unclear and is based upon limited data. Hypothesis A comprehensive and integrated personalised approach, including recognition of cardiac energetics parameters extracted from a personalised mathematical model, mapped to patient activity, has the potential to improve diagnosis and the planning and timing of interventions. Aims This project seeks to implement a simple, personalised, mathematical model of patients with aortic stenosis (AS), which can ‘measure’ cardiac work and power parameters that provide an effective characterisation of the demand on the heart in both rest and exercise conditions and can predict the changes of these parameters following an intervention. The specific aims of this project are: • to critically review current diagnostic methods • to evaluate the potential role of pre- and post-procedural measured patient activity • to implement a simple, personalised, mathematical model of patients with AS • to evaluate the potential role of a clinical decision support system Methods Twenty-two patients with severe AS according to ESC criteria were recruited. Relevant clinical, imaging, activity monitoring, six-minute walk test, and patient reported data were collected, before and early and after treatment. Novel imaging techniques were developed to help in the diagnosis of AS. A computational model was developed and executed using the data collected to create non-invasive pressure volume loops and study the global haemodynamic burden on the left ventricle. Simulations were run to predict the haemodynamic parameters both during exercise and following intervention. Modelled parameters were validated against clinically measured values. This information was then correlated with symptoms and activity data. A clinical decision support tool was created and populated with data obtained and its clinical utility evaluated. Outcomes The results of this project suggest that the combination of imaging and activity data with computational modelling provides a novel, patient-specific insight into patients’ haemodynamics and may help guide clinical decision making in patients with AS