International Journal of Bioinformatics and Biomedical Engineering
Articles Information
International Journal of Bioinformatics and Biomedical Engineering, Vol.4, No.2, Jun. 2018, Pub. Date: Oct. 9, 2018
Identification of Arrhythmia in Electrocardiogram (ECG) Using Statistical Tools and Non-Linear Analysis
Pages: 31-44 Views: 279 Downloads: 93
Authors
[01] Mishuk Mitra, Faculty of Electrical Engineering and Information Technology, Otto von Guericke University, Magdeburg, Germany.
[02] Sumit Chakrabarty, Faculty of Electrical Engineering and Information Technology, Otto von Guericke University, Magdeburg, Germany.
[03] Md Shamim Mia, Faculty of Electrical Engineering and Information Technology, Otto von Guericke University, Magdeburg, Germany.
[04] Atia Rahman, Department of Electrical and Electronic Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh.
Abstract
ECG signal of a human heart is a self-similar object, Analysing the ECG is a useful tool for diagnosing heart diseases. Using mathematical methods can be identify and distinguish specific states of heart pathological conditions. For this the QRS complex in the ECG is used in many methods to distinguish between the healthy person and the ailing one. Classical techniques have been used to address this problem such as the analysis of electrocardiogram (ECG) signals for arrhythmia detection. The aim of this research is to use the chaotic model to analyse and define the arrhythmia that may be observed in an ECG signal. Chaos may be defined as the pattern that lies between the determinism and randomness of a system. Some techniques of nonlinear analysis are recently having its popularity to many researchers working on nonlinear data for which most mathematical models produce intractable solutions. These non-linear techniques are applied on the bit-to-bit interval (BBI) and instantaneous heart rate (IHR) that are derived from the sample ECG records from MIT-BIH database. The results found from this work are analysed to identify arrhythmia in the examined ECG record. These methods will be applied to a large class of long duration data sets and it is expected that the proposed technique will provide a better result by comparison with others to detect the abnormality of ECG signal.
Keywords
ECG Arrhythmia, Beat Classification, Instantaneous Heart Rate (IHR), Root Mean Square of Successive Difference (RMSSD)
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