Name
Title | ||
|---|---|---|
A Hybrid Deep Cnn Model For Abnormal Arrhythmia Detection Based On Cardiac Ecg Signal |
Abstract | ||
|---|---|---|
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21030951 | SENSORS |
Keywords | DocType | Volume |
electrocardiogram signal, arrhythmia, classification, 2D CNN, MIT-BIH, arrhythmia database | Journal | 21 |
Issue | ISSN | Citations |
3 | 1424-8220 | 1 |
PageRank | References | Authors |
0.48 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amin Ullah | 1 | 109 | 11.60 |
| Sadaqat Ur Rehman | 2 | 1 | 0.48 |
| Shan-Shan Tu | 3 | 17 | 3.80 |
| Raja Majid Mehmood | 4 | 3 | 2.55 |
| Fawad | 5 | 1 | 0.48 |
| Muhammad Ehatisham-ul-Haq | 6 | 27 | 6.73 |