Cardiovascular diseases are the main cause of death across the globe. Therefore, it is critical to detect them at an early stage, while an electrocardiogram (ECG) presents a widely used tool for making diagnosis. There is a large number of ECG devices on the market, with different features that suit a wide range of users, but simultaneously it poses a problem for the end user because they have a difficulty in selecting the appropriate device for their exact needs. Current trends in the usage of contemporary mobile ECG devices are identified and analyzed. In this study authors reviewed numerous mobile ECG devices with either FDA approval or CE mark that are currently available on the market. All listed devices are presented with all technical features that could be found. Furthermore, an interactive sunburst diagram for the selection of mobile ECG devices is created, and published, to help everyone easily make a selection.

Coronary artery disease (CAD) is one of the most common causes of death. This paper presents a study on the applicability of open-source data mining tools for the development of medical risk scores and their regional character for CAD detection. The presented methodology is based on the performance evaluation of diverse data mining techniques on well-known sample extracted from UCI machine-learning dataset containing four clinical databases with data about 920 patients collected at four different world regions. Although CAD related subset of UCI dataset is used too many times in previous researches, we decided to use it to prove some features which are not explored in the literature extensively. The CAD prediction results show that different factors have different impact on the results in chosen regions and therefore decision support systems should cover smaller regions and should not be general. Achieved results are validated on a new dataset collected in the 5th region. Finally, the study shows that effective clinical models can be developed using open-source solutions, paving the way to the introduction of custom clinical practice based on data mining in the developing countries, which are most affected by the coronary disease.

The burden of chronic disease and associated disability present a major threat to financial sustainability of healthcare delivery systems. The need for cost-effective early diagnosis and disease prevention is evident driving the development of personalized home health solutions. The proposed solution presents an easy to use ECG monitoring system. The core hardware component is a biosensor dongle with sensing probes at one end, and micro USB interface at the other end, offering reliable and unobtrusive sensing, preprocessing and storage. An additional component is a smart phone, providing both the biosensor's power supply and an intuitive user application for the real-time data reading. The system usage is simplified, with innovative solutions offering plug and play functionality avoiding additional driver installation. Personalized needs could be met with different sensor combinations enabling adequate monitoring in chronic disease, during physical activity and in the rehabilitation process.