Diabetes mellitus (DM) affects more than 366 million people around the world (Alam et al. 2009). One of the serious clinical complications of DM is cardiovascular autonomic neuropathy (CAN), which gradually results in abnormalities of heart rate (HR) control and vascular dynamics (Kuehl and Stevens 2012; Vinik and Ziegler 2007). The occurrence of confirmed CAN in diabetes patients is approximately 20%, and increases up to 65% with age and diabetes duration (Spallone et al. 2011). Ewing et al. reported a mortality 228rate of 53% after 5 years in a cohort of diabetic patients with CAN versus 15% in the control group (i.e., diabetic patients without CAN) (Ewing et al. 1980). CAN progression may lead to severe postural hypotension, exercise intolerance, enhanced intraoperative instability, increased incidence of silent myocardial infarction, and ischemia (Vinik and Ziegler 2007). Around 75% of people with diabetes die from cardiovascular disease such as heart attack and stroke, which includes autonomic neuropathy as a cause (Krolewski et al. 1977; Nathan et al. 2005). Early detection of CAN in diabetic patients and intervention is therefore of prime importance to reduce the increased mortality of diabetes patients. The presence and severity of CAN are difficult to diagnose at the subclinical stage due to the absence of overt symptoms. As a result, it creates a potential negative impact on the quality of life of patients and those with the preclinical asymptomatic disease (Spallone et al. 2011; Vinik and Ziegler 2007). To enable early treatment intervention and improved outcomes requires accurate and sensitive measures for detecting subclinical CAN.
History
Chapter number
10
Pagination
227-248
ISBN-13
9781482243482
ISBN-10
1482243482
Language
eng
Publication classification
B Book chapter, B1 Book chapter
Copyright notice
2018 by Taylor & Francis Group, LLC
Extent
23
Editor/Contributor(s)
Jelinek HF, Cornforth DJ, Khandoker AH
Publisher
Taylor & Francis
Place of publication
Boca Raton, Fla
Title of book
ECG time series variability analysis : engineering and medicine