A nonintrusive C-Band sensing system successfully captured breathing-induced chest movements to identify Kussmaul breathing, achieving accuracies between 97.82% and 99.76% across subjects.
Does a C-Band microwave sensing platform accurately detect Kussmaul breathing in diabetic patients?
A novel C-Band microwave sensing platform demonstrates high accuracy in detecting breathing patterns, offering a potential nonintrusive method for real-time monitoring of Kussmaul breathing in diabetic ketoacidosis.
Tactile Internet can be seen as the next wave of innovation, which has the characteristics of low latency, high reliability, availability, and security. Tactile Internet promises its application in various fields ranging from industry automation and transport systems to healthcare, education, and gaming. Therefore, in this paper, we present the application of tactile Internet in healthcare by considering the diabetic ketoacidosis (DKA) as a use case, which is a potentially life-threatening complication of diabetes mellitus. Severe DKA causes Kussmaul breathing, which is a life-threatening condition that usually requires hospitalization. However, to date, no real-time monitoring system exists for DKA. In this paper, we propose a nonintrusive breathing monitoring system using the C-Band sensing technique. The system exploits a microwave sensing platform to capture breathing-induced chest movements in diabetic patients. A peak detection algorithm is used to acquire the respiratory rate and extent, identified as normal breathing or Kussmaul breathing. The real-time respiratory rate and extent could be used to diagnose DKA in diabetic patients. The accuracy for all the subjects are 99.76%, 98.69%, 97.82%, 99.06%, and 98.42%, respectively; therefore, the reliability of the system is demonstrated.
Yang et al. (Wed,) conducted a other in Diabetic ketoacidosis (DKA) (n=5). Nonintrusive breathing monitoring system using C-Band sensing was evaluated on Accuracy of detecting respiratory rate and extent (normal vs Kussmaul breathing). A nonintrusive C-Band sensing system successfully captured breathing-induced chest movements to identify Kussmaul breathing, achieving accuracies between 97.82% and 99.76% across subjects.