Biomedical Research

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Detecting and classifying ECG abnormalities using a multi model methods

Heart Abnormalities can be diagnosed accurately by examining and investigating the ECG signals carefully. Numerous techniques were proposed in the earlier research works in the area of arrhythmia detection by utilizing traditional methods of classification. Also, in the earlier researches Chaos theory and non-linear analysis are applied to characterizing the ECG signals. In this paper, the proposed method detects and analyses the abnormalities with respect to P, Q, R and S peak values. The entire proposed work is divided into three phases, whereas in the first phase, the data acquisition is applied to the real time ECG data. In the second phase, preprocessing is applied to the ECG signal data. In the third phase, features are extracted from ECG signals and finally from the extracted features, the abnormal peaks are classified to identify the abnormality of the ECG signals. The data acquisition is achieved from the relevant database, then preprocesses the data using Base Line Correction (BLC), inflection point detection using Powerline interference, Feature Extraction by GLCM method and finally features are classified and detecting the abnormality using the SVM classifier. The data set used in this paper is taken from the world-famous MIT-BIH Arrhythmia database and other international ECG signal databases.

Author(s): Mahalakshmi Ponnusamy, Sundararajan M