Data-driven and Domain Knowledge Based Forecasting of Acute Events

Forecasting the onset of the acute events has become critical for the development of smart screening cyber-physical system in healthcare. Investigations on the triggering mechanism of various acute disorders onset in patients are still not well established, despite the urgent demanding of the onset forecasting. The utilization the morphological-temporal features of the surface electrocardiogram (ECG) or heart rate variability (HRV) features significantly requires the explanation from clinical perspectives, especially about the trigger modes of onset and the disease pathologies.  Therefore, this work aims to construct data-driven analytic forecasting method, gain a profound understanding of the physiological dynamics of the cardiovascular system for the forecast prior (i.e. 5 mins to 60 mins in advance) the onset of proximal atrial fibrillation and myocardial infarction attacks before the clinical symptoms actually happen. Such research will provide a reliable clinical decision-making support for physicians.