Abstract

MORENO-REVELO, Mónica et al. Unsupervised analysis applied to the detection cardiac arrhythmias. Enfoque UTE [online]. 2017, vol.8, suppl.1, pp.257-272. ISSN 1390-6542.  https://doi.org/10.29019/enfoqueute.v8n1.125.

An arrhythmia is a pathology that consists on altering the heartbeat. Although, the 12-lead electrocardiogram allows evaluation of the electrical behavior from heart to determine certain pathologies, there are some arrhythmias that are difficult to detect with this type of electrocardiography. In this sense, it is necessary the use of the Holter monitor because it facilitates the records of the heart electrical activity for long periods of time, it is usually 24 up to 48 hours. Due to the extension of the records provided by the monitor, it is common to use computational systems to evaluate diagnostic and morphological features of the beats in order to determine if there is any type of abnormality. These computational systems can be based on supervised or unsupervised pattern recognition techniques, however considering that the first option requires a visual inspection about the large number of beats present in a Holter record, it is an arduous task, as well as it involves monetary costs. Consequently, throughout this paper we present the design of a complete system for the identification of arrhythmias in Holter records using unsupervised pattern recognition techniques. The proposed system involves stages of pre-processing of the signal, segmentation and characterization of beats, as well as feature selection and clustering. In this case, the technique k-means is used. These steps are applied within the framework of a segment-based methodology that improves the detection of minority classes. Additionally, initialization criteria are considered, which allow to enhance quality measures, especially sensitivity. As a result, it is determined that using k-means with the max-min initialization and a number of groups equal to 12, it is possible to obtain the best results, with values of: 99.36%, 91.31% and 99.16% for accuracy, sensitivity and specificity, respectively.

Keywords : Clustering; Segment-based methodology; Initialization; Quality measures..

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