Abstract:
The electrocardiogram (ECG) is the signal that represents time variations of the electric
activity of the heart. It constitutes an effective tool for diagnostic of heart anomalies, for this
reason we are in need for long recordings of ECG that reflect the state of the heart but this
will cause problems of storing or transmission of the ECG to a distant interpretation center,
this has motivated research works towards compression
Recently, Neural Networks have occupied a great place in signal processing, specifically
Recurrent Neural Networks because they are capable to adapt to time variations of non linear
and non stationary signals such as the ECG signal.
In this regard, a compression algorithm via parameters extraction using Recurrent Neural
Networks has been developed and tested on electrocardiography signals of the “MIT-BIH
Arrythmia Data Base” and results obtained are presented, discussed and compared with those
of some most recent algorithms of ECG compression.
