Detecting subclinical diabetic cardiac autonomic neuropathy by analyzing ventricular repolarization dynamics

Imam, Mohammad Hasan, Karmakar, Chandan, Jelinek, Herbert F., Palaniswami, Marimuthu and Khandoker, Ahsan H. 2015, Detecting subclinical diabetic cardiac autonomic neuropathy by analyzing ventricular repolarization dynamics, IEEE Journal of biomedical and health informatics, vol. 20, no. 1, pp. 64-72, doi: 10.1109/JBHI.2015.2426206.

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Title Detecting subclinical diabetic cardiac autonomic neuropathy by analyzing ventricular repolarization dynamics
Author(s) Imam, Mohammad Hasan
Karmakar, ChandanORCID iD for Karmakar, Chandan
Jelinek, Herbert F.
Palaniswami, Marimuthu
Khandoker, Ahsan H.
Journal name IEEE Journal of biomedical and health informatics
Volume number 20
Issue number 1
Start page 64
End page 72
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-04
ISSN 2168-2208
Keyword(s) coupling
Cardiac Autonomic Neuropathy (CAN)
subclinical CAN
ventricular repolarization (VR)
linear parametric modeling
heart rate variability (HRV)
ECG derived respiration (EDR)
Summary In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals), and respiration [calculated by ECG-derived respiration (EDR)]. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR, and EDR time series extracted from the first 5 min of the recorded ECG signal. Results showed that the EDR-based models gave a significantly higher fitting value (p < 0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory-information-based models. Moreover, the QT-RR-EDR model fitting values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR-based model significantly distinguished ECAN and DCAN groups from the NCAN group (p < 0.05) with large effect sizes (Cohen's d > 0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR-based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate QT-RR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions, such as diabetic CAN.
Language eng
DOI 10.1109/JBHI.2015.2426206
Field of Research 090609 Signal Processing
090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
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