TY - GEN
T1 - Time-series network analysis for detecting cardiac autonomic neuropathy using RR interval data
AU - Karmakar, Chandan
AU - Khandoker, Ahsan
AU - Jelinek, Herbert
AU - Palaniswami, Marimuthu
PY - 2013
Y1 - 2013
N2 - 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 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.
AB - 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 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.
UR - http://www.scopus.com/inward/record.url?scp=84894155607&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84894155607
SN - 9781479908844
T3 - Computing in Cardiology
SP - 97
EP - 100
BT - Computing in Cardiology 2013, CinC 2013
T2 - 2013 40th Computing in Cardiology Conference, CinC 2013
Y2 - 22 September 2013 through 25 September 2013
ER -
