| A convolutional neural network approach for Ecg signal classification |
| Paper ID : 1141-ICEEM2023 (R1) |
| Authors: |
|
Hadeel Adel Mohamed * Electronic and Electrical communication Menoufia university |
| Abstract: |
| This paper, is concerned with the classification of ECG signals for two purpose, anomaly detection and identification of persons. A deep learning approach is presented for recognition from ECG histograms. We present an efficient deep learning approach for ECG signal classification. It depends on creating spectrograms of EG signals and treating them as images to be classified with deep neural networks. This approach has been tested on MIT-BIH arrythmia database. It managed to achieve a high classification accuracy of normal and abnormal cases. Simulation results ensure the ability to differentiate between ECG signals which are normal or abnormal and also the ability to distinguish between persons based on ECG signals. The accuracy reached 100% in both scenarios. |
| Keywords: |
| ECG, Classification, Deep learning, Neural Networks, CCNA |
| Status : Paper Accepted |
