St Cross's College
CDT Postgraduate Studentship in Healthcare Innovation (RCUK Digital Economy Programme grant number EP/G036861/1)
Crowd-sourcing cardiac data using Bayesian voting in resource-constrained environments
Cardiovascular disease currently accounts for 30 percent of global deaths and is predicted to remain as the single leading global burden of injury and death for the next 20 years, particularly affecting lower- and middle-income countries. Electrocardiogram (ECG) is the gold standard for cardiac abnormality screening. The lack of trained experts to provide accurate reading of the ECGs compounds the problem. Multiple noncolluding experts/annotators and/or algorithms can be combined to improve the accuracy of class labelling tasks. My work is focused on building a stand-alone ECG annotation system which consists of two parts: 1) Crowd-sourcing ECG annotations and provides training to non-experts to identify cardiac arrhythmia. 2) Act as a back-end server to an electronic medical healthcare record system and provides instantaneous diagnosis of cardiac arrhythmia by combining multiple annotators and/or algorithms using a Variational Independent Bayesian classifier combination framework.