Heart Rate Variability measured during rest and after orthostatic challenge to detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification and Regression Tree model

• Published in: Technology and health care Vol. 30; no. 2; pp. 361 - 378
• Main Authors: Rathod, Shashikant, Phadke, Leena, Chaskar, Uttam, Patil, Chetankumar
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2022; Sage Publications Ltd
• Subjects: Adult; Algorithms; Autonomic nervous system; Autonomic Nervous System Diseases; Classification; Complications; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - complications; Diabetic Neuropathies - diagnosis; Dysautonomia; EKG; Female; Heart rate; Heart Rate - physiology; Humans; India; Learning algorithms; Machine learning; Male; Regression analysis; Regression models; Sensitivity; Variability; India; Heart Rate Variability (HRV); orthostatic challenge; machine learning; Type 2 diabetes mellitus (T2DM); classification and regression tree (CART); autonomic dysfunction
• DOI: 10.3233/THC-213048
• ISSN: 0928-7329; 1878-7401

BACKGROUND: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing’s test and resting Heart Rate Variability (HRV) indices. OBJECTIVE: Resting HRV has limited use in screening due to its large intra and inter-individual variations. Therefore, a combined approach of resting and orthostatic challenge HRV measurement with a machine learning technique was used in the present study. METHODS: A total of 213 subjects of both genders between 20 to 70 years of age participated in this study from March 2018 to December 2019 at Smt. Kashibai Navale Medical College and General Hospital (SKNMCGH) in Pune, India. The volunteers were categorized according to their glycemic status as control ( n = 51 Euglycemic) and T2DM ( n = 162). The short-term ECG signal in the resting and after an orthostatic challenge was recorded. The HRV indices were extracted from the ECG signal as per HRV-Taskforce guidelines. RESULTS: We observed a significant difference in time, frequency, and non-linear resting HRV indices between the control and T2DM groups. A blunted autonomic response to an orthostatic challenge quantified by percentage difference was observed in T2DM compared to the control group. HRV patterns during rest and the orthostatic challenge were extracted by various machine learning algorithms. The Classification and Regression Tree (CART) model has shown better performance among all the machine learning algorithms. It has shown an accuracy of 84.04%, the sensitivity of 89.51%, a specificity of 66.67%, with an Area Under Receiver Operating Characteristic Curve (AUC) of 0.78 compared to resting HRV alone with 75.12% accuracy, 86.42% sensitivity, 39.22% specificity, with an AUC of 0.63 for differentiating autonomic dysfunction in non-diabetic control and T2DM. CONCLUSION: It was possible to develop a Classification and Regression Tree (CART) model to detect autonomic dysfunction. The technique of percentage difference between resting and orthostatic challenge HRV indicates the blunted autonomic response. The developed CART model can differentiate the autonomic dysfunction using both resting and orthostatic challenge HRV data compared to only resting HRV data in T2DM. Thus, monitoring HRV parameters using the CART model during rest and after orthostatic challenge may be a better alternative to detect autonomic dysfunction in T2DM as against only resting HRV.