A Remote Medical Monitoring System for Heart Failure Prognosis

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.

Mobile Phone Apps to Support Heart Failure Self-Care Management: Integrative Review

“…Ten of the 18 studies included in this review had small sample sizes of 40 or fewer participants (8 studies assessed usability of the mobile system for refinement or further development of an algorithm [31-40]), 4 studies had sample sizes of 41 to 99 [41-44], and 4 studies had 100 or more participants (but fewer than 200) [45-48]. Four of the 18 studies reviewed were pilot RCTs [32,34,42,44], and only 2 RCTs had 100 or more participants [47,48].…”

“…All 18 studies used mHealth technology via mobile phones or tablets as the medium for self-care intervention. A total of 11 studies reported outcome data (Table 1), 7 studies reported only usability of the mobile technology (Table 2), and 1 study developed an algorithm to differentiate HF but did not provide usability or efficacy data [40].…”

A Platform for e-Health Control and Location Services for Wandering Patients

“…It enables storing large data sets for heart rate, stress, and Require: newSample of HR Ensure: Identification of abnormality events (1) if newSample is normalRead then (2) staAbnormality is false (3) if staAbnormality is true then (4) if sampleTime − staAbnTim > gapTime then (5) Abnormal Episode (6) else (7) newSample (8) end if (9) else (10) staAbnormality es true (11) staAbnTim it is equal to sampleTime (12) end if (13) end if ALGORITHM 1: Identification of AHREs. Mobile Information Systemslocation monitoring services, which can be accessed when required.…”

“…e web platform was developed to browse through the stored information so that caregivers can have access to Require: Data to be monitored I � GPS, HR, SL { } not empty Ensure: Identification of abnormality episodes (1) while I ≠ null do (2) if connection BLE is true then (3) CMA-HR-SL receives I and executes anomaly identification algorithm (4) else (5) connection BLE is false (6) end if (7) Establish WiFi connection and send I to external storage service (8) External storage service provides I to web platform (9) Carer visualizes episodes of abnormality of the patient from web platform (10) end while ALGORITHM 2: Monitoring and location process.…”

“…is is the case of the research [5], where the authors designed and implemented a remote monitoring medical system for the prediction and management of heart failure, predicting the risk of heart failure as a function of changes in pressure arterial, and body weight of patients who are in a noninvasive environment. Integration of several functions into a monitoring application has also been achieved, such as a visualization interface of data relating to monitored parameters, graphically presenting the patient's ECG [6].…”