Clinical Feature Classification for Chronic Heart Failure and Construction of a Safe Mechanism for Rehabilitation using Internet-of-Medical-Thing Devices

Abstract

Heart failure (HF) is a complex syndrome without an objective definition. It has become a serious problem in public health policies because of the increased prevalence, high cost of treatment, frequent re-hospitalization and high mortality. Neither strict standards for HF classification nor single-type treatments are currently available. The non-specific clinical symptoms make diagnosis at early stages difficult, leading to deterioration and hospitalization. The use of advanced medical techniques and newly developed medicines may decrease mortality, but many HF patients still have a low quality of life because of insufficient muscular endurance and limited activities. Recent reports have shown that exercise programs contribute to the recovery of cardiac functions and improve clinical results for most HF patients. However, excessive, intense exercise may increase the risk of death, particularly for cardiac-related patients. In this study, different HF types are categorized and a safe, customized mechanism for self-exercise training integrating Internet-of-Medical-Thing devices and cloud computing technologies is proposed. The detected biometric features of the HF patients are linked to the personal communication devices of the patients and doctors, a cloud server system and the hospital medical information system. The proposed system mainly collects heart rate and metabolic equivalent features in a real-time manner from the Internet-of-Medical-Thing devices worn by patients. Measured data are dynamically compared to customized maximum limitations that are defined by rehabilitation physicians according to the patient's cardio-pulmonary exercise testing record in the hospital. A prototype system was successfully developed and validated with several test cases and showed excellent performance at an affordable cost. The proposed mechanism provides a customized platform for HF patients to pursue a better quality of life, based on prognostic exercise prescription using a safe self-exercise training mechanism.