Research Article
Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques
Table 5
Performance comparison of the proposed model with the existing works.
| | S. No. | Year | Author | Methodology | F1 score (%) |
| A | 1 | 2017 | Manuel et al. [10] | Multiclass SVM | 73 | B | 2 | 2017 | Rajpurkar et al. [11] | The deep CNN model has 34 layers that map ECG signal samples into arrhythmia heartbeat classes. | 79.9 | C | 3 | 2017 | Coppola et al. [12] | Hierarchical classification model | 78.55 | D | 4 | 2017 | Neha et al. [13] | A LSTM network, which learns patterns directly from precomputed QRS complex features that classify ECG signals | 78 | E | 5 | 2017 | Schwab et al. [14] | Ensemble RNN with the LSTM attention model | 79 | F | 6 | 2017 | Andreotti et al. [15] | ResNet CNN | 79 | G | 7 | 2017 | Jiménez-Serrano et al. [16] | Feedforward neural network (FFNN) | 77 | H | 8 | 2022 | (present work) | CNN-ResNet model | 80.58 | Hybrid-ResNet and LSTM (bidirectional) | 80.08 | ResNet and RBF | 80.20 |
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