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Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes

Karmakar, Chandan, Jelinek, Herbert F., Khandoker, Ahsan, Tulppo, Mikko, Makikallio, Timo, Kiviniemi, Anitti, Huikuri, Heikki and Palaniswami, Marimuthu 2015, Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes, in EMBC 2015: Proceedings of the IEEE Engineering in Medicine and Biology Society 2015 Annual International Conference, IEEE, Piscataway, N.J., pp. 2367-2370, doi: 10.1109/EMBC.2015.7318869.

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Title Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes
Author(s) Karmakar, Chandan
Jelinek, Herbert F.
Khandoker, Ahsan
Tulppo, Mikko
Makikallio, Timo
Kiviniemi, Anitti
Huikuri, Heikki
Palaniswami, Marimuthu
Conference name IEEE Engineering in Medicine and Biology Society. Annual International Conference (37th : 2015 : Milan, Italy)
Conference location Milan, Italy
Conference dates 25-29 Aug. 2015
Title of proceedings EMBC 2015: Proceedings of the IEEE Engineering in Medicine and Biology Society 2015 Annual International Conference
Editor(s) [Unknown]
Publication date 2015
Start page 2367
End page 2370
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) HRV analysis
HRV algorithms
Poincare plot derived features
Autonomic nervous system
HEART-RATE-VARIABILITY
MYOCARDIAL-INFARCTION
AUTONOMIC NEUROPATHY
RATE DYNAMICS
MORTALITY
Summary Diabetes mellitus is associated with multi-organ system dysfunction including the cardiovascular and autonomic nervous system. Although it is well documented that post-infarct patients are at higher risk of sudden cardiac death, diabetes adds an additional risk associated with autonomic neuropathy. However it is not known how the presence of diabetes in post-infarct patients affects cardiac rhythm. The majority of HRV algorithms for determining cardiac inter-beat interval changes describe only beat-to-beat variation determined over the whole heart rate recording and therefore do not consider the ability of a heart beat to influence a train of succeeding beats nor whether or how the temporal dynamics of the inter-beat intervals changes. This study used Poincaré Plot derived features and incorporated increased lag intervals to compare post-infarct patients with no history of prior infarct with or without diabetes and found that for the nondiabetic post-infarct patients only increased lag of short term correlation (SD1) predicted mortality, whereas in the diabetic post-infarct group only long-term correlations (SD2) significantly predicted mortality at a follow-up period of eight years. Temporal dynamics measured as a complex correlation measure (CCM) was also a significant predictor of mortality only in the diabetic post-infarct cohort. This study highlights the different pathophysiological progression and risk profile associated with presence of diabetes in a post-infarct patient population at eight year follow-up.
ISBN 9781424492701
ISSN 1094-687X
Language eng
DOI 10.1109/EMBC.2015.7318869
Field of Research 090304 Medical Devices
090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084622

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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