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In addition, this paper also provides a comparative study of several methods proposed by researchers in extracting the ECG signal characteristic. This paper discusses several techniques and transformations previously proposed in the literature to extract characteristics of an ECG signal. All these techniques and algorithms have their advantages and limitations. The proposed schemes were based mainly on fuzzy logic, artificial neural network (ANN), genetic algorithm (GA), vector support machines (SVM) and other signal analysis techniques. Recently, numerous investigations and techniques have been developed to analyse the ECG signal. The value of the amplitudes and the intervals of the segment P-QRS-T determines the functioning of the heart of each human being. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for further analysis. A cardiac cycle in an ECG signal consists of the P-QRS-T waves. ĮCG Feature Extraction plays an important role in the diagnosis of most heart diseases.
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In this project we will implement a system that first extracts ECG characteristics and on the basis that we will find the location and amplitude of ECG signal details. The improvement of precise and fast methods for the automatic extraction of ECG characteristics is of paramount importance, particularly for the examination of long recordings.
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Most of the clinically useful information on the ECG originates from the intervals and amplitudes defined by their characteristics (characteristic peak and time duration). The ECG is a realistic record of the direction and magnitude of the electrical commotion that is generated by depolarisation and re-polarisation of the atria and ventricles.
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Bae, Egil Tai, Xue-Cheng Bertozzi, Andrea L.: Simplified energy landscape for modularity using total variation (2018) Berthouze, Luc Miller, Joel C.: Edge-based compartmental modelling of an SIR epidemic on a dual-layer static-dynamic multiplex network with tunable clustering (2018)