ECG classification using Deep CNN and Gramian Angular Field
Youssef Elmir Yassine Himeur Abbes Amira
This paper study provides a novel contribution to the field of signal
processing and DL for ECG signal analysis by introducing a new feature
representation method for ECG signals. The proposed method is based on
transforming time frequency 1D vectors into 2D images using Gramian Angular
Field transform. Moving on, the classification of the transformed ECG signals
is performed using Convolutional Neural Networks (CNN). The obtained results
show a classification accuracy of 97.47% and 98.65% for anomaly detection.
Accordingly, in addition to improving the classification performance compared
to the state-of-the-art, the feature representation helps identify and
visualize temporal patterns in the ECG signal, such as changes in heart rate,
rhythm, and morphology, which may not be apparent in the original signal. This
has significant implications in the diagnosis and treatment of cardiovascular
diseases and detection of anomalies.