Detection of Abnormalities in Cardiac Rhythm Using Spiking Neural Networks

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have been increasingly attracting attentions in many different fields. Healthcare is one of the areas that has greatly benefited from the advances in AI/ML. This includes a wide range of applications such as medical data interpretation, disease or abnormality detection or prediction, monitoring specific health condition and medical data management. On the other hand, patients can also take advantage of available healthcare devices to be more conscious of their health status and increase their quality of life. However, implementing AI/ML algorithms on resource-constrained wearable devices is challenging. One way to tackle this problem is to exploit the neuromorphic computing solutions such as Spiking Neural Networks (SNNs), which are more energy efficient than conventional neural networks because of their more similar function to how the brain works. In this thesis project, we investigate the working-mechanism of these networks, how we can design, train and use them for the detection of abnormalities in cardiac function.