SciELO - Scientific Electronic Library Online

 
vol.8 número15SISTEMA DE TOCOCARDIOGRAFÍA DIGITAL CON PROCESAMIENTO EN UNA FPGA PARA EL MONITOREO MATERNO-FETAL índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • No hay articulos similaresSimilares en SciELO
  • En proceso de indezaciónSimilares en Google

Compartir


Revista Ingeniería Biomédica

versión impresa ISSN 1909-9762

Resumen

RODRIGUEZ, C.A. et al. CLASSIFICATION OF PREMATURE VENTRICULAR CONTRACTION BEATS BASED ON UNSUPERVISED LEARNING METHODS. Rev. ing. biomed. [online]. 2014, vol.8, n.15, pp.51-58. ISSN 1909-9762.

Cardiovascular diseases are the principal cause of mortality in the world, so that the development of algorithms that detect cardiac arrhythmias in real time has become an important field of research. The development of these algorithms has led to the improvement of wearable cardiac devices. This paper presents the performance of two algorithms based in unsupervised learning methods for the detection of Premature Ventricular Contraction in the ECG signal. The beats are extracted from MIT-BIH databases, which were preprocessed and segmented by the UPB’s Dynamic Cardiovascular research group. The Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and a proposed hybrid method are implemented for the feature extraction and dimension reduction, from which 8 feature spaces are generated and tested. Kmeans and Self Organizing Maps are developed and compared in terms of accuracy and computational cost. Specificity of 96.22 % and sensitivity of 95.94% with 79.41µs per beat are accomplished. The results show that these methods can be implemented in applications of real time arrhythmia detection because of their low computational cost.

Palabras clave : Arrhythmia; Premature Ventricular Beat; Discrete Wavelet Transform; Principal Component Analysis; Kmeans; SOM.

        · resumen en Español | Portugués     · texto en Español     · Español ( pdf )