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A Model-Based Approach for Arrhythmia Detection and Classification

  • Hongzu Li
  • Pierre Boulanger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)

Abstract

Automatic real-time ECG patterns detection and classification has great importance in early diagnosis and treatment of life-threatening cardiac arrhythmia [7]. In this paper, we developed an algorithm which could classify abnormal heartbeat at more than 85% accuracy. The ECG data of this research are provided by MIT-BIH Arrhythmia Database from Physionet. We extracted seven features from each ECG record to represent the ECG signal. Furthermore, Support Vector Machine and Multi-Layer Perceptron Neural Network are used for classification. We were able to achieve over 85% accuracy and with only 10% difference between sensitivity and specificity.

Keywords

ECG Machine learning Pattern recognition Support vector machine Neural network 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of AlbertaEdmontonCanada

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