## 3 Materials and Methods
### SOTA Models  
The SOTA models utilized to validate the proposed methodologies and the proposed model (HRIDM) are LeNet-5, AlexNet, VGG16, ResNet50, Inception, and LSTM. These are prominent and commonly utilized models for various tasks, especially signal and image classification. LeNet-5 was the first basic convolutional neural network (CNN) model introduced in 1998 by [PERSON] et al. [40]. It consists of seven layers: three Convolution (Conv) layers, two pooling layers (average pooling), and two fully connected layers (FC) along with sigmoid or tanh activation functions. This was the first CNN model successfully trained on the MNSIT dataset for a digit recognition task. However, due to its structure containing fewer layers, it is not suitable for more complex tasks. AlexNet was first introduced in 2012 by [PERSON] et al. [41], which introduced the ReLU activation function and used dropout layer to overcome overfitting [42]. AlexNet consists of eight layers: five Conv layers, among which the first, second, and fifth are followed by max-pooling layers, and three fully connected layers. All layers use ReLU activations except the output layer, which included the SoftMax activation function. To capture hierarchies in the data, this model makes use of the filters' depth and stride. However, because it has an extensive number of parameters, computation costs are high. VGG16 is a prominent deep CNN model introduced in 2014 by the Visual Geometry Group [43] at Oxford University. It consists of 16 layers: 13 Convolutional layers and three fully connected layers. VGG16 utilizes small 3 \(\times\) 3 filters in Conv layers throughout its structure, and max-pooling layers are applied after some of the Conv layers to downsample the feature maps. Due to its deeper architecture, it is very effective in capturing fine details of the features and capable of performing more complex tasks effectively. Its specialty lies in its simple and uniform structure, which is easy to design and extend. However, its limitation is the slow training time due to large parameters and depth. The ResNet50 model, developed in 2015 by [PERSON] et al. [44], which introduced the concept of the residual block, contains 50 layers and is capable of resolving the vanishing gradient problems in deeper networks. The residual block is mathematically defined as \((y=f(x)+x)\), where \(f(x)\) is the CNN output within the block and \(x\) is the input. This model introduced the concept of skip connections and allows the gradient to pass directly through these connections. It also introduced bottleneck design in its structure of the  
Figure 4: Segmented and preprocessed 12-lead ECG signals. residual blocks consisting of 1 \(\times\) 1, 3 \(\times\) 3, and 1 \(\times\) 1 Conv layers. The 50 layers in the ResNet50 model with residual blocks enable the capture of more complex patterns. However, its limitation is the high computational cost due to its complex and deep structure. The Inception model is a deep CNN model that introduced the concept of inception modules to enhance the efficiency and accuracy of DL models. The first version, Inception v3, was introduced in 2015 by [PERSON] et al. [45], and uses parallel Conv layers within the same modules to capture very fine details at different levels. Inception v3 has 48 layers with inception modules which consist of multiple Conv layers with different parallel filters (1 \(\times\) 1, 3 \(\times\) 3, 5 \(\times\) 5) along with max-pooling layers. It is capable of capturing very complex patterns with a smaller number of patterns as compared to similar DL models. LSTM (Long Short-Term Memory) networks, introduced in 1997 by [PERSON] and [PERSON] [46], are especially designed for sequential data patterns. LSTM was introduced to resolve the problem of short-term memory by incorporating gates and states. An LSTM network consists of many cells, including a cell state \((c_{t})\) and a hidden state \((h_{t})\), as well as gates such as the input gate \((i_{t})\), forget gate \((f_{t})\), and output \((o_{t})\) gate.