Rathod Shashikant, Phadke Leena, Chaskar Uttam, Patil Chetankumar
Department of Instrumentation and Control Engineering, College of Engineering, Pune, India.
Department of Physiology, Smt. Kashibai Navale Medical College and General Hospital, Pune, India.
Technol Health Care. 2022;30(2):361-378. doi: 10.3233/THC-213048.
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.
背景：根据世界卫生组织的数据，在未来几年中，每十名成年人中就有一人会患2型糖尿病（T2DM）。自主神经功能障碍是T2DM的重要并发症之一。自主神经功能障碍通常通过标准的尤因氏试验和静息心率变异性（HRV）指标进行评估。 目的：由于静息HRV在个体内和个体间的差异较大，其在筛查中的应用有限。因此，本研究采用了静息和直立位激发HRV测量相结合的方法，并结合机器学习技术。 方法：2018年3月至2019年12月，共有213名年龄在20至70岁之间的男女受试者在印度浦那的什里·卡希拜·纳瓦尔医学院和综合医院（SKNMCGH）参与了本研究。志愿者根据血糖状态分为对照组（n = 51，血糖正常）和T2DM组（n = 162）。记录静息和直立位激发后的短期心电图信号。根据HRV工作组指南从心电图信号中提取HRV指标。 结果：我们观察到对照组和T2DM组在时间、频率和非线性静息HRV指标上存在显著差异。与对照组相比，T2DM组中通过百分比差异量化的对直立位激发的自主神经反应减弱。通过各种机器学习算法提取静息和直立位激发期间的HRV模式。分类与回归树（CART）模型在所有机器学习算法中表现出更好的性能。与仅使用静息HRV相比，其准确率为84.04%，灵敏度为89.51%，特异性为66.67%，受试者工作特征曲线下面积（AUC）为0.78，而仅使用静息HRV区分非糖尿病对照组和T2DM中的自主神经功能障碍时，准确率为75.12%，灵敏度为86.42%，特异性为39.22%，AUC为0.63。 结论：有可能开发一种分类与回归树（CART）模型来检测自主神经功能障碍。静息和直立位激发HRV之间的百分比差异技术表明自主神经反应减弱。与仅使用T2DM中的静息HRV数据相比，开发的CART模型可以使用静息和直立位激发HRV数据来区分自主神经功能障碍。因此，在静息和直立位激发后使用CART模型监测HRV参数可能是检测T2DM中自主神经功能障碍的更好选择，而不仅仅是静息HRV。
Annu Int Conf IEEE Eng Med Biol Soc. 2013
J Assoc Physicians India. 2024-7
Zotero 插件