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Automatic classification of electrocardiogram (ECG) signals is vital for clinical diagnosis of heart disease.
ECG signals are segmented after baseline; high-frequency noise is removed and fiducial points are detected.
The 12-lead ECG is so called because it produces twelve ECG signals.
They typically use vectors to represent ECG signals; however, this is not a natural representation of ECG signals because a large amount of useful structural information is discarded.
Most studies on ECG classification methods have targeted only 1- or 2-lead ECG signals.
Electrocardiogram (ECG) signals are contaminated with different artifacts and noise sources which increase the difficulty in analyzing the ECG signals and obtaining accurate diagnosis of heart diseases.
They can then measure their heart rates, respiration, ECG signals and temperature.
The system provided high-precision ECG signals by a simple procedure.
Besides, it may be time-consuming to check these ECG signals manually.
Illustrative applications on ECG signals from the MIT-BIH arrhythmia database are presented.
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Large storage capacity permits recording of all ECG signals for a day at a time.
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