[home] [Personal Program] [Help]

---

Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

---

Yaowen Zhang ()
Peter H. Veltink ()
Dirk W. Donker ()
Ying Wang ()

Abstract:
Diabetes mellitus causes significant cardiovascular complications with incidences that reach pandemic proportions worldwide. Among the diabetic sequelae, diabetic cardiomyopathy has important clinical implications as it may progress towards symptomatic heart failure and irreversible cardiac remodeling. The late manifestations of diabetes strongly limit prognosis, while putting an enormous burden on health care expenses [1]. Despite the increasing awareness of disease prevention and timely medical treatment, there is still no effective way to detect diabetic cardiomyopathy at its earliest possible, potentially reversible stages. Therefore, it is urgently needed to develop advanced vital monitoring tools that allow to gain more insights into individual patient’s pathologically changed vital parameters. This could help to identify early signs of diabetic cardiovascular disease in people at a high risk of developing diabetic cardiomyopathy. To our best knowledge, such dedicated monitoring systems that could be applied for daily life usage have not been developed. From a pathophysiological perspective, early manifestations of diabetic cardiomyopathy are characterized by the imbalance of autonomic nervous system which is defined as hyperactive sympathetic activities and hypoactive parasympathetic activities as is reflected by heart rate variability (HRV) changes [2]. From this, HRV, especially the detailed analysis of its daily life associated dynamicity, can likely serve as an important candidate marker to indicate early signs of diabetic cardiomyopathy. Therefore, we aim to develop a remote monitoring system for the early detection of diabetic cardiomyopathy that can be easily applied in patients’ daily life. Our monitoring model with the inputs of daily physical activity, such as, walking, running, and cycling, should ultimately predict cardiac responsiveness to the individual's activity (notably HRV) and detect 'warning patterns' of patients’ actual cardiac responses. The wearable sensors used to track body movement signals will be inertial measurement units (IMUs), which contain three-axis accelerometers and three-axis gyroscopes. The cardiac activity prediction model will be built as a combination of both physiological models and data driven models. Decision support algorithms to analyze disease risks will be generated as based on the data derived from pre-diabetic and diabetic individuals in order to derive early 'warning patterns' of cardiac responses. References [1] Jia, G., Hill, M. A., & Sowers, J. R. (2018). Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. Circulation research, 122(4), 624–638. https://doi.org/10.1161/CIRCRESAHA.117.311586 [2] P. Van Kessel, D. De Boer, M. Hendriks, and A. M. Plass, “Measuring patient outcomes in chronic heart failure: Psychometric properties of the Care-Related Quality of Life survey for Chronic Heart Failure (CaReQoL CHF),” BMC Health Serv. Res., vol. 17, no. 1, pp. 1–7, Aug. 2017, doi: 10.1186/S12913-017-2452-4/FIGURES/1.