# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### DL Methodology  
#### 3.3.1 Model  
The design of DL models is crucial to the pipeline of DL-based ECG arrhythmia classification. The DL models have multi-level or multi-layer structures, and each level or layer can be regarded as a feature extractor that can learn how to better summarize signal characteristics [48]. Based on the intrinsic property of the major feature extractor within the neural networks, the DL classification models considered in the selected studies can  
Figure 3: Distribution of preprocessing methods used by selected studies in each year. (**a**) Trend of different types of denoising methods; (**b**) Trend of different types of data augmentation methods. mainly be categorized into the following types: convolutional neural networks (CNNs), recurrent neural networks (RNNs), including the long short-term memory (LSTM) and bidirectional LSTM (BiLSTM), transformer, \"hybrid\" which refers to combinations of different DL models, and \"others\" corresponding to less popular models such as restricted Boltzmann machines and deep-belief networks. The detailed analysis of those DL models for ECG arrhythmia classification is as follows. * CNN is a DL model widely used in image classification, signal analysis, and natural language processing [48]. Each layer of CNN usually contains a convolutional filter followed by pooling operations to extract both local and global features [49]. Depending on the number of filtering directions of the convolutional filters in the spatial domain, the CNN can be further categorized into 1D CNN and 2D CNN. Specifically, the filters in 1D CNN and 2D CNN move along one and two filtering directions, i.e., feature dimensions, respectively. In general, 1D CNN is suitable for raw or denoised ECG signals, which only have one single feature dimension. For instance, in [50], an adaptive 1D CNN is proposed for ECG classification and anomaly detection at any sampling rate of ECG signals to avoid hand-crafted feature extraction. In [51], a lightweight 1D CNN considering channel shuffle over the group and depth-wise convolutions is designed, where 2-s ECG signal segments are considered as model input [37]. In [38], the 1D CNN is leveraged to classify 2, 5, and 20 types of heart diseases where few-shot learning is considered to deal with the small-size of the dataset. On the other hand, 2 DCNN mainly takes into account the image-like input, such as the spectrogram and scalogram of ECG signals. In [52], the 2D scalogram is obtained by transforming the 1D ECG signals having 500 samples to the wavelet domain using continuous wavelet transform. Then the 2D scalogram is regarded as a 3-channel color image with a size of 227 \(\times\) 227 in the spatial domain. A classic 2D CNN, i.e., AlexNet [53], is used to classify ECG signals. In [54], the plot of 1D ECG recordings is directly transformed to 2D gray-level images with a size of 15 \(\times\) 15 which are then fed as input for the 2D CNN. In [55], a multi-lead CNN takes multi-lead ECG as the matrix input, where the sub-2D convolution and lead asymmetrical pooling are exploited to extract multi-scale features. Due to simpler operation compared to 2D convolution, 1D CNN often contains fewer learnable parameters and has higher computation speed, making it suitable for real-time ECG classification and is often easier to be deployed in hardware. * RNN/LSTM/BiLSTM  
Taking into account the temporal correlation of feature sequences, RNN is a type of DL structure that considers the input as a time series. As ECG signals are time series in nature, their temporal correlation within the signals could potentially better reveal the sign of their categories. For typical RNNs, the information in their hidden layer at the current moment does not only depend on the current input but relies on the information at the previous time instance [56]. In this way, the RNN is more sensitive to the temporal features of the input sequence and is advantageous in capturing hidden temporal information in ECG features [56].