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Segment clustering for holter recordings analysis

Peluffo-Ordóñez, Diego H. Y Castro, Andres Y Rodriguez, Jose L. (2015) Segment clustering for holter recordings analysis. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, 1-5 junio 2015, Elche,España.

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In this work, an efficient non-supervised algorithm for clustering of ECG signals is presented. The method is assessed over a set of records from MIT/BIH arrhythmia database with different types of heartbeats, including normal (N) heartbeats, as well as the arrhythmia heartbeats recommended by the AAMI, usually found in Holter recordings: ventricular extra systoles (VE), left and right branch bundles blocks (LBBB and RBBB) and atrial premature beats (APB). The results are assessed by means the sensitivity and specificity measures, taking advantage of the database labels. Also, unsupervised performance measures are used. Finally, the performance of the algorithm is in average 95%, improving results reported by previous works of the literature.

Tipo de Elemento: Conferencia o Taller artículo (Paper)
Asunto: Q Ciencias > QA Mathematics > QA75 Electronic computers. Computer science
Division: Facultad de Ingeniería > Programa de Ingeniería Electrónica > Productividad
Depósito de Usuario: Andres Pantoja
Fecha Deposito: 13 Sep 2018 21:30
Ultima Modificación: 13 Sep 2018 21:30

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