This work represents an advance in sophisticated methods used to detect severe cardiac autonomic neuropathy (CAN). It applies clustering based on a graph model to ECG biosignal processing results in order to optimise classification performance. Severe CAN represents a particularly significant neurological problem in diabetes healthcare as it requires urgent intervention to reduce the risk of sudden cardiac death. The introduction of a new Clustering System Based on Graphs (CSBG) combined with heart rate features determined from recorded ECG biosignals was intended as a means of enhancing the effectiveness of the diagnosis of severe CAN. Here we study a novel heart rate descriptor – Allan exponents (AE) to determine the effectiveness of CSBG and compare the results with performance of other classification and clustering systems available in Sage. The best outcomes were obtained by CSBG in combination with AE, which improved the F measure of classification performance to 0.92 and outperformed several other classification and clustering systems in our experiments.
|Number of pages||8|
|Journal||International Journal of Computer & Software Engineering|
|Publication status||Published - 19 Dec 2016|