Work

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

Year: 2013

Type: conference-paper

Abstract: 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: J 5 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 mopt = 270 and nopt = 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.

Source: Charles Sturt University Research Output (CRO)

Authors: Chandan Karmakar, Ahsan H. Khandoker, Herbert F. Jelinek, Marimuthu Palaniswami

Institutions: The University of Melbourne, Khalifa University of Science and Technology

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W2119255614

FWCI:

Cites: 14

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Related to: 10