Cardiovascular autonomic neuropathy (CAN) is highly prevalent and a serious complication in patients with diabetes mellitus. In this study, we investigate the effect of changing the degree and data length on network properties (transition asymmetry and network efficiency) to differentiate negative CAN (NCAN) subjects from definite CAN (DCAN). Forty-one patients with Type 2 diabetes mellitus were included in the study: 15 patients had definite CAN (DCAN), whilst the remaining 26 were negative for CAN (NCAN), being without clinical signs and symptoms of CAN. Symbolic Aggregate approximation (SAX) was used as the discretization procedure to convert the heart rate variability (HRV) time-series signal to network. The optimal degree (m) and data length (n) were found to be m opt = 270 and n opt = 200 respectively with leave-one-out accuracy of 85.37% using transition asymmetry (A(G)) and network efficiency (EF) indexes. Both, A(G) and EF indexes are found to be a potential parameter for detecting CAN in diabetes.
History
Volume
40
Pagination
97-100
Location
Zaragoza, Spain
Start date
2013-09-22
End date
2013-09-25
ISSN
2325-8861
eISSN
2325-887X
ISBN-13
9781479908844
Publication classification
E Conference publication, E2.1 Full written paper - non-refereed / Abstract reviewed
Title of proceedings
Proceedings of the Computing in Cardiology Conference 2013