Challenges for development of an ECG m-Health solution

Abstract

Abstract. This paper presents the challenges to develop a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easily wearable and mobile ECG biosensor, a cloud-based data centre, smartphone and web application. A significant part of the system is the 24h health monitoring and care provided by expert cardiac physicians. The system predicts potential heart attack and sends risk alerts to the medical experts for assessment. If a potential heart attack risk exists, an emergency ambulance is being called with the coordinates of the cardiac patient wearing the sensor. The timely reaction can prevent serious tissue damage or even death to the users of the system. Our goal in this paper is to elaborate the challenges we met and solutions we have developed for development of an m-Health mobile application for detection of abnormalities in the ECG and alerting of a heart attack. The problems analyzed address Low Power Bluetooth connections, number conversion and transmission, decision making on what to be locally processed and what computations to be offloaded to cloud, software architecture, type of initial filtering for obtaining sufficient quality of the ECG signal, and visualization approach with relatively small processing requirements.

Stimulating Intellectual Activity with Adaptive Environment (SMILE)

“…An example of integration challenges about ECG wearable sensors is given by Gusev et. al [4]. On the other side, the MIndGym approach [10,12] is realised using EEG-based signals and recommendations about Mind cognitive skills of elderly with the goal to provide mind gymnastics to keep the mental health and elderly brain in a good shape.…”

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

“…For real-time monitoring, it is important to use energy-efficient devices and communication technologies to allow for long-term monitoring. This challenge was highlighted by many researchers [159,160,279]. Sampling algorithms were used by the authors of [159,160] to help conserving energy.…”

“…Sampling algorithms were used by the authors of [159,160] to help conserving energy. Also, the authors of [146,279] used Bluetooth as a low-energy communication protocol. Furthermore, data dimensionality reduction techniques can be used to reduce data size, thus alleviating its processing, which will support monitoring durability.…”

“…Gusev et al [279] highlighted a few challenges in handling visualization for continuous ECG monitoring. Mainly, the problems were related to handling different refreshing rate requirements via multiple platforms installed on displaying devices with low processing requirements.…”