Data mining in ECG data

Dimitrios Tseles, Costas Alafodimos, Soultana Vasileiadou, Gregory Nikolaou


: Electrocardiography (ECG) is a data source that can contain valuable knowledge for the body function and can reveal information for several diseases. It has been used across the years to diagnose cardiovascular diseases and to prognose upcoming abnormalities. Along with the evolution of data processing methods, data mining algorithms in ECG data have been used in the last three decades. This work investigates this area of research with the aim to realize the advances through time and present current research trend. The area of data mining is first presented and a brief introduction of the main algorithms is given. The focus of this work is placed on the different frameworks and models used in the area, namely signal processing approaches, morphological based processing and data mining. Literature shows a trend towards real-time diagnosis based on portable devices. Technology evolution provides portable computational power that can be used for real-time data processing and a number of research studies in this topic are presented. This work concludes with some remarks in this area and its future evolvement.



Medical Data Mining, ECG Data, Arrhythmia, Real-time diagnosis

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