Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.
Abstract. This paper presents a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easy wearable and mobile ECG biosensor, a cloud based data center, smartphone and web application. A significant part in 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, 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.
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.
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