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Mobile Information Systems
Volume 2015, Article ID 406327, 12 pages
http://dx.doi.org/10.1155/2015/406327
Research Article

A Remote Medical Monitoring System for Heart Failure Prognosis

1Shanxi Cardiovascular Hospital, Taiyuan 030000, China
2First Hospital of Shanxi Medical University, Taiyuan 030000, China
3Taiyuan City Central Hospital, Taiyuan 030000, China
4Taiyuan Maixinyun Healthcare Management Co. Ltd., Taiyuan 030000, China
5Huaxin Consulting Co., Ltd., Hangzhou 310014, China
6Shanxi Academy of Medical Sciences and Shanxi Dayi Hospital, Taiyuan 030000, China

Received 16 June 2015; Revised 25 August 2015; Accepted 14 September 2015

Academic Editor: Wenyao Xu

Copyright © 2015 Liangqing Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Remote monitoring of heart disease provides the means to keep patients under continuous supervision. In this paper, we introduce the design and implementation of a remote monitoring medical system for heart failure prediction and management. The three-part system includes a patient-end for data collection, a medical data center as data storage and analysis, and a doctor-end to diagnosis and intervention. The main objective of the system is to prognose the occurrence risk of heart failure (HF) confirmed by the level of N-terminal prohormone of brain natriuretic peptide (NT-proBNP) based on the changes of the patients’ (systolic and diastolic) blood pressure and body weight that are measured noninvasively in a home environment. The prediction of HF and non-HF patients was achieved by a structured support vector machine (SVM) classification algorithm. With the present system, we also proposed a scoring method to interpret the long-term risk of HF. We demonstrated the efficiency of the system with a pilot clinical study of 34 samples, where the NT-proBNP test was used to help train the prediction model as well as check the prediction results for our system. Results showed an accuracy of 79.4% for predicting HF on day 7 based on daily body weight and blood pressure data acquired over 30 days.