*4.5. Sensitivity of the ECG Network Architecture*

In order to further assess the sensitivity of the proposed approach with different configurations, we carried out additional experiments to show the effect of using different configurations of the ECG branch net on the overall accuracy. Considering that the 2D-CNN architecture proves its superiority over the FC and 1D-CNN as shown in the previous sections, we reported the experiments that cover applying different configurations using 2D-CNN architecture. We tested 8 different configurations as described in Table 8. Let us consider the configuration #8, which is shown in Figure 6c: (2 fc = (128, 1024), 2 blocks MBConv (64, 128), fc = 128), this means we use two consecutive fully-connected layers of size 128 and 1024, respectively, in addition to using two consecutive MBConv blocks, of depth 64 and 128, respectively, and finally one fully-connected layer of size 128. The second fully-connected layer fc (1024) means that the feature vector is reshaped into (32 × 32 × 1) as shown in Figure 6c.

From the reported results in Table 8, we note the following points. Removing the first fully-connected layer fc (128) in configuration #1, degraded the accuracy (91.90%), whereas increasing the feature map in configuration #2 by replacing the second fully-connected layer fc (1024) with fc (4096); i.e., the feature vector is reshaped into (64 × 64 × 1); will not significantly improve the accuracy (93.56%). Furthermore, changing the number and sizes of MBConv blocks up to 3 (configurations #6 & #7) or down to 1 (configurations #3, #4, & #5), produces better accuracies up to 95.56% in configuration #4. In the proposed configuration #8, we used 2 MBConv blocks, in which the networks achieved the second best accuracy of 95.32%.


**Table 8.** Classification accuracy of 2D-CNN network by applying three different configurations for ECG architecture.
