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Electrocardiogram (ECG) signals are used to analyze the cardiovascular activity in the human body and have a primary role in the diagnosis of several heart diseases. The QRS complex is the most important and distinguishable component in the ECG because of its spiked nature and high amplitude. Automatic detection and delineation of the QRS complex in ECG is of extreme importance for computer aided...
Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body and has a primary role in the diagnosis of several heart diseases. The QRS complex is the most distinguishable component in the ECG. Therefore, the accuracy of the detection of QRS complex is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim...
The aim of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial premature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and premature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector...
A reversible watermarking algorithm with high data-hiding capacity has been developed for electrocardiogram (ECG) signal based on wavelet transforms. In electrocardiogram signal, the energy is concentrated in QRS complex waves. So the selection of wavelet coefficients for hiding should avoid making QRS complex waves distort obviously. The algorithm hides bits in the expansion of selected coefficients...
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