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Synthesis of Neural Network Approaches Used for ECG Classification (Paperback)
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Synthesis of Neural Network Approaches Used for ECG Classification (Paperback)
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Cardiovascular diseases represent the most frequent cause of death
in Europe. Consequently, their diagnoses appear a vital task. In
cardiology unit, ECG signal still remains the dominant used tools
for arrhythmia and heart diseases analysis. In fact, the ECG is a
non-invasive tool to explore the functional heart sate. It is an
electrical signal varying according to the electrical heart state.
From the ECG signal, some significant parameters can be extracted.
Generally, the durations and the shapes of the different waves are
taken as bio-indicators of certain cardiac anomalies. In fact,
manual detection and classification of ECG waves is a difficult and
annoying task especially for the analysis of the long recordings as
in Holters and ambulatory cases. Besides, a detailed analysis of
12-leads ECG used to identify the presence or the absence of the
cardiac malfunction is also irritating. However, and due to large
number of patients in intensive care units, the need for continuous
heart activity monitoring is necessary requiring therefore the
automatic analysis of the ECG signal.
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