Big data reduction using RBFNN: A predictive model for ECG waveform for eHealth platform integration

The main challenge of big data processing includes the extraction of relevant information, from a high dimensionality of a wide variety of medical data by enabling analysis, discovery and interpretation. These data are a useful tool for helping to understand disease and to formulate predictive models in different areas and support different tasks, such as triage, evaluation of treatment, and monitoring. In this paper, a case study based on a predictive model using the radial basis function neural network (RBFNN) combined with a filtering technique aiming the estimation of electrocardiogram (ECG) waveform is presented. The proposed method revealed it suitability to support health care professionals on clinical decisions and practices.