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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51148

Title: Clinical Feature Classification for Chronic Heart Failure and Construction of a Safe Mechanism for Rehabilitation using Internet-of-Medical-Thing Devices
Authors: Shao-Jie Hsu
Shih-Syun Lin
Tun-Wen Pai
Chao-Hung Wang
Min-Hui Liu
Chi-Wen Cheng
Dong-Yi Chen
Kuo-Li Pan
Shyh-Ming Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: chronic heart failure
ejection fraction
heart rate
metabolic equivalent
Date: 2017-03
Issue Date: 2018-11-14T08:17:03Z
Publisher: Acta Polytechnica Hungarica
Abstract: 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
Relation: 14(1)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51148
Appears in Collections:[資訊工程學系] 期刊論文

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