Nowadays, due to the unhealthy lifestyle and high stress in modern society, cardiovascular disease (CVD) has become a disease of the majority. As an important instrument for diagnosing CVD, electrocardiography (ECG) is used to extract useful information about the functioning status of the heart. To help clinicians better utilize the ECG data, various systems have been proposed in last decades. One of the key issues in these system is the analysis of ECG data. In this domain, cluster analysis is a commonly applied approach to gain an overview of the data, detect outliers or pre-process before further analysis. In recent years, to provide better medical care for CVD patients, the new-generation cardiac telehealth system, which could monitor patients’ ECG in a real-time manner, has...[ Read more ]

Nowadays, due to the unhealthy lifestyle and high stress in modern society, cardiovascular disease (CVD) has become a disease of the majority. As an important instrument for diagnosing CVD, electrocardiography (ECG) is used to extract useful information about the functioning status of the heart. To help clinicians better utilize the ECG data, various systems have been proposed in last decades. One of the key issues in these system is the analysis of ECG data. In this domain, cluster analysis is a commonly applied approach to gain an overview of the data, detect outliers or pre-process before further analysis. In recent years, to provide better medical care for CVD patients, the new-generation cardiac telehealth system, which could monitor patients’ ECG in a real-time manner, has draw a great attention from both academia and industry. In these systems, the collected ECG data is transmitted to a remote server and analysed in a real-time manner. However, the extremely large volume and high update rate of data in these telehealth systems have made cluster analysis a challenging work. In this paper, we design and implement a novel parallel system for clustering massive ECG stream data based on the MapReduce framework. In our approach, a global optimum of clustering is achieved by merging and splitting clusters dynamically. Meanwhile, a good performance is gained by distributing computation over multiple computing nodes. According to the evaluation, our system not only provides good clustering results but also has an excellent performance on multiple computing nodes.