Abstract
Cardiovascular autonomic neuropathy (CAN) is one of the most overlooked complications associated with diabetes. It is characterized by damage in the autonomic nerves regulating heart rate and vascular compliance. Ewing battery is currently the diagnostic tool of choice but is unable to detect sub-clinical CAN and requires patient cooperation. In addition, appropriate timing (day/night) of CAN diagnostic test was not explored in the past. Therefore, a novel approach is proposed herein to investigate the feasibility of using heart rate variability (HRV) features over 24 hours embedded within machine learning algorithms to provide a complete screening for patients suffering from CAN. 24-hour Holter ECG data were acquired from a Bangladeshi cohort (n = 95 patients [75 Diabetic and 25 healthy]). HRV features were extracted from every 5-minute segment of the HRV signal and used as input to four machine learning algorithms for hourly training and testing. A complete hierarchical step by step diagnosis procedure (4 tests) was developed; namely test 1 to check for being healthy or diabetic; test 2 to check for any microvascular complications (including neuropathy such as CAN, peripheral neuropathy (DPN), nephropathy (NEP), and retinopathy (RET)) or not; test 3 to check for presence of only CAN; test 4 to check for combined or multiple complications along with CAN. The highest levels of performance were achieved with accuracy measures of 85.5% (test 1 - convolutional neural network (CNN)), 98.5% (test 2 - CNN), 98.3% (test 3 - one-class support vector machines (SVM)), and 90.9% (test 4 - random forest). Hours 7:00 AM and 7:00 PM were found to be most significant in the diagnosis of CAN in diabetic patients (test 1, 3, and 4). Early screening of CAN by our proposed models could help primary healthcare centers stratify the risk leading to early treatment in preventing sudden cardiac death due to silent myocardial infarction. The approach is considered to be simple and effective, especially for under-resourced clinical settings.
Highlights
Diabetes is a chronic disease that occurs when high levels of glucose are accumulated in the blood
The discrimination accuracy between diabetic and control patients for the convolutional neural network (CNN) model reached 85.5% compared to 80.0%, 82.2%, and 80.0% for support vector machine (SVM), Random forest (RF), and RUSBOOST, respectively
STUDY LIMITATIONS this study shows that machine learning-based models have performed efficiently in diabetes and associated microvascular complications diagnosis, it has a number of shortcomings
Summary
Diabetes is a chronic disease that occurs when high levels of glucose are accumulated in the blood. Alkhodari et al.: Screening cardiovascular autonomic neuropathy in diabetic patients with microvascular complications can not effectively use the produced insulin to move sugar from the blood into cells and subsequently, use it for energy [1], [2]. This interruption in the secretion and transport of this hormone in the body raises glucose levels in the blood causing serious damage to nerves and blood vessels. It was estimated that more than 422 million people are suffering from diabetes in 2014, reaching a total of 1.5 million deaths directly caused by this disease in 2019
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
