Screening Cardiovascular Autonomic Neuropathy in Diabetic Patients With Microvascular Complications Using Machine Learning: A 24-Hour Heart Rate Variability Study

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.

Keywords

• Diabetes
• cardiovascular autonomic neuropathy (CAN)
• 24-hour electrocardiography (ECG)
• heart rate variability (HRV)
• machine learning
• one-class support vector machine (SVM)