Recognition of Cardiac Arrhythmia by Means of Beat Clustering on ECG-Holter Recordings

The development of bio-signal analysis systems,mostly, has become amajor research field due to technological progress in signal processing. Electrocardiography (ECG) had been amongst the most studied type of bio-signals for several decades. Research on this type of signals has become an important tool for the diagnosis of cardiac disorders. Because of its simplicity, low cost and a non-invasive nature it is still widely used despite newer available techniques. This chapter deal with the problem of long-term recording analysis corresponding to ECG signals of Holter recordings. The motivation for studying this issue focuses on the development of methods for cardiac arrhythmia analysis to identify particular events occurring at specific periods of time. Such events are associated to cardiac disorders that may become potentially harmful to the patient. The developed methods are aimed at further building up of specialized equipment that will provide clinical monitoring for both the patient and the specialist, as well as the support for real time diagnosis.The above mentioned will decrease mortality rates regarding heart problems specially for people living in rural areas. This technology will benefit them to have access to a quicker and efficient specialized medical diagnostics. This chapter focuses on analyzing two major aspects of Holter recordings: The first one corresponds to the large amount of data stored in such recordings, reaching up to 100.000 heartbeats for its evaluation, which becomes a hard task for the specialist to assess the information and to decide what heartbeats are important for a determined analysis. There are cases where only a few beats allow to identify a certain pathology or to prevent deadly diseases. Therefore, a detailed analysis of the complete record is needed. The second aspect corresponds to the intrinsic characteristics of the signal, such as heart rate variability, morphological variety, among others. They may result from problems in the cardiac system or the patient’s physical and physiological characteristics. In addition, the electrical nature of ECG signals and its transmission to electronical devices increase the noise sensitivity, which can completely alter the diagnostic information contained in the signal, changing the training processes in the identification of cardiac pathologies. 12

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