STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microcontroller Engineering and Applications

Survey of Techniques for Clustering and Classification of ECG using WEKA

  1. Nidhi Gupta
  2. Prem Ranjan

Abstract

ECG analysis can be done for automatic detection of abnormality in cardiac activity. This can be helpful in generating alert for saving a precious life. Various techniques have been proposed in literature for feature detection and classification such as; fuzzy logic methods, artificial neural networks (ANN), and support vector machines (SVM), wavelet transform, Hilbert transform, etc. Recently, numerous researches and techniques have been developed for analyzing the ECG signal on WEKA platform. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting features from an ECG signal using WEKA platform. In addition, this paper also provides a comparative study of various methods proposed by researchers in extracting the features and classification of ECG signal

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