## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Model** & \
\begin{tabular}{c} **Noise Reduction** \\ **or Augmentation** \\ \end{tabular} & **Feature** &
\begin{tabular}{c} **Additional** \\ **Approach** \\ \end{tabular} & **Test Score** \\ \hline \multirow{4}{*}{[22]} & Hand-Designed & \multirow{4}{*}{NA} & \multirow{4}{*}{NA} & Utilized Second & \multirow{4}{*}{38.2\%} \\  & Recurrent & & & Neural Model & \\  & Convolutional Neural & & & & Neural Model & \\  & Network (RCNN) & & & & \\  & Squeeze-and- & & & & \\  & excitation networks for & & & & \\  & ECG classification & & & & \\  & (SE-ECGNet) & & & & \\  & Exponentially Dilated & & & & \\  & Causal Convolutional & & & & \\  & Neural Network & & & & Neural Model & \\  & (ED-CCNN) & & & & \\  & 18-Layer Residual & & & & \\  & Convolutional Neural & & & & \\  & Network (ResNet-18) & & & & \\  & Adversarial & & & & \\  & Multi-Source Domain- & & & & Adversarial & \\  & Generalization & & & & domain & \\  & (AMDG) & & & & generalization & \\  & Deep Residual Neural & & & & \\  & Network with Scatter & & & & \\  & Transform & & & & \\  & (DResNet-ST) & & & & \\  & Ensemble SD-ResNet & & & & \\  & Model & & & & \\  & Model & & & & \\  & Modified ResNet with & & & & \\  & a Squeeze-and- & & & & Grid-Search, & \\  & Excitation Layer & & & & Bespoke Weighted & \\  & (MResNet-SE) & & & & Accuracy Metric & \\  & Wide and Deep & & & & \\  & Transformer Neural & & & & \\  & Network (WDTNN) & & & & \\  & **Proposed** & **HRIDM** & **NA** & **NA** & **NA** & **50.87\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._scores compared to our proposed model. Among them, ref. [18] had the lowest score with 12.2%, but due to the correct evaluation strategy, they secured a place in the official ranking. In this work, the authors used Polyphase Filter Resampling techniques for preprocessing of the ECG data. No additional feature extraction techniques were used. With this approach, they classified into 24 categories out of 27 and utilized 75 epochs for training. Among all the reviewed studies, most authors [18; 20; 24; 25; 26], utilized CNNs in their model design, either directly or indirectly. The second most utilized model was ResNet, as employed by many authors [19; 27; 28; 29; 21], in their methodology, directly or indirectly. The analysis concluded that all the top four studies, including ours, which secured the first four places, utilized residual structure in the model design. Based on the observation of preprocessing techniques such as denoising or data augmentation, it can be concluded that the studies [19; 22; 29; 21] did not utilize any of these techniques for preprocessing of the ECG data. Among these studies, only [29] achieved a better score than proposed technique. While studies [21; 23; 26; 30] employed additional feature extraction techniques to enhance the ECG abnormality classifications, only [30] achieved a better score as compared to our proposed techniques. Additionally, only studies [19; 20; 22; 29] did not utilize any preprocessing or feature extraction techniques, although they employed some additional approaches for result enhancements. Among them, only study [29] achieved a better score as compared to our proposed methodology. Although our proposed method achieved fourth place with a 50.87% test score as compared with the official rankings, its strength lies in its efficiency and simplicity. Unlike other studies, we did not employ any feature extraction, denoising, additional strategies, or data augmentation in our proposed methodology. Due to the integration of residual and inception blocks in our model architecture, its performance is exceptionally good without relying on common preprocessing steps. Among the state-of-the-art classifiers, the highest accuracy achieved was 40.6% using the Inception network. However, the results obtained using these established architectures did not yield satisfactory performance. The DNNs, LSTM, and LeNet-5 struggled to classify beyond a single class.