Deep Learning Methods in Internet of Medical Things for Valvular Heart Disease Screening System

Abstract

The heart is one of the most important organs of the human body. It circulates blood throughout the human body and delivers oxygen and nutrients to all the organs for metabolism. Cardiac muscle contraction results in blood circulation, which maintains the body temperature at approximately 37 degrees C. If the cardiac function is abnormal, then the body temperature will be affected. The cardiac function degenerates as the human body ages, and the degeneration can occasionally result in cardiovascular diseases. When the Internet of Medical Things is integrated into heart disease screening systems to detect heart diseases, people can perform self-examinations to evaluate whether their hearts exhibit irregularities for early heart disease detection. STM32 is used in this study as the main Internet-of-Medical Things controller and is combined with the Internet of Things devices-a sphygmomanometer cuff, temperature sensor, and pulse sensor-for instrument control and data acquisition. This assembly is used to develop a valvular heart disease screening system, whose structure incorporates deep learning for the development of fitting models and analysis. An experiment is performed where blood flow is blocked temporarily and released to observe changes in the surface temperature of the fingertip skin, and the blood supply capability of the heart is assessed indirectly based on the temperature change curve. Eighteen subjects were recruited in the experiment, where one subject exhibited cardiac valve insufficiency and arrhythmia. In the experiment, temperature curve variation data are successfully obtained from the healthy subjects, whereas the temperature curve irregularities of the patient with cardiac valve insufficiency are identified. This subject's temperature range throughout three test steps is smaller by within 0.52 degrees C compared with those of most of the other subjects. In addition, during blood blocking and release, the overall temperature curve decreases, whereas some curves escalate first before plummeting slowly. The data analysis results show that the temperature curve variation and values of Subject 2 are similar to those of Subject 10, suggesting the incidence of valvular heart disease. This valvular heart disease screening system can successfully analyze and assess the characteristic signal values of patients with valvular heart disease.