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Time-series network analysis for detecting cardiac autonomic neuropathy using RR interval data

conference contribution
posted on 2013-12-01, 00:00 authored by Chandan KarmakarChandan Karmakar, A Khandoker, H Jelinek, M Palaniswami
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

Publisher

Institute of Electrical and Electronics Engineers

Location

Zaragoza, Spain

Place of publication

Piscataway, N.J.

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

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