NDLI: A new approach to extract features from ECG signals

Content Provider	IEEE Xplore Digital Library
Author	Sadi, M.S. Debnath, S. Al Jami, F.F. Mahmudur Rahman, G.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Khulna Univ. of Eng. & Technol. (KUET), Khulna, Bangladesh (Sadi, M.S.; Debnath, S.; Al Jami, F.F.; Mahmudur Rahman, G.M.)
Abstract	Electrocardiogram (ECG) is the record of electrical and muscular movement of the heart over a period of time which is useful for diagnosing many cardiac diseases. Due to the increase in mortality rate associated with cardiac diseases, automatic detection of these diseases is highly required. Hence, feature extraction and de-noising of ECG signals are highly useful in cardiology. This paper describes a novel approach for feature extraction in order to recognize trusty heart rhythm. This method consists of two components: data preprocessing and feature extraction. At pre-processing stage, baseline wander removal and power line inference have been used for removing baseline wander and de-noising the signal respectively. At the second stage, R wave is detected and then Q, S, T and P waves are extracted based on R wave. In this paper, Wavelet transformation is used for power line inference. The experimental studies show that the proposed method provides 96.79% accuracy and 5 features recognition with reduced computational complexity.
Starting Page	189
Ending Page	194
File Size	697773
Page Count	6
File Format	PDF
ISBN	9781467392563
e-ISBN	9781467392570
DOI	10.1109/EICT.2015.7391944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-10
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms De-noising Radiation detectors Baseline Wander Signal processing algorithms Electrocardiography ECG Feature extraction QRS Complex R Wave Diseases
Content Type	Text
Resource Type	Article

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