## 2 Materials and Methods
### Analysis  
The 1-dimensional convolutional neural network (1D-CNN) [27; 28] and the recurrent neural network (RNN) [29; 30] were trained during 100 epochs. Data on 508 ECG segments were divided into training and validation sets with a 75:25 ratio (381 vs. 127 ECG segments). Sensitivity, specificity, F1-score and accuracy were introduced as criteria for validating the trained models. Sensitivity is the proportion of true positives among real positives, whereas specificity is the proportion of true negatives among real negatives. The F1-score is the harmonic mean of sensitivity and specificity. Accuracy is the proportion of correct predictions (both true positives and true negatives) among all cases, i.e., 125 ECG segments. Here, a neural network is a network of \"neurons\", i.e., information units combined through weights. Usually, the neural network has one input layer, one, two or three intermediate layers and one output layer. Neurons in a previous layer connect with \"weights\" in the next layer, and these weights represent the strengths of connections between neurons in a previous layer and their next-layer counterparts. This process starts from the input layer, continues through intermediate layers and ends in the output layer (feedforward operation). Then, learning happens: These weights are accommodated based on how much they contributed to the loss, the difference between the actual and predicted final outputs. This process starts from the output layer, continues through intermediate layers and ends in the input layer (backpropagation operation). The two operations are replicated until a certain expectation is met regarding the accurate diagnosis of the dependent variable. In other words, the performance of the neural network improves as long as its learning continues [31]. A deep neural network is a neural network with a large number of intermediate layers, e.g., 5, 10 or even 1000. The deep neural network is referred to as \"deep learning\" given that learning \"deepens\" for complex representations based on various elements of its architecture design [31]. Specifically, certain types of deep learning models, so-calledconvolutional neural networks (CNNs) and their recurrent neural network counterparts (RNNs), have emerged as dominant deep learning models in the past decade. The CNN has convolutional layers, in which a kernel passes across input data and performs \"convolution\", that is, computes the dot product of its own elements and their input-data counterparts. When the input data have width and height, the kernel also moves across the two dimensions. Here, convolution (\"2D convolution\") detects the shape of the input data (e.g., circle), which agrees with the shape of the kernel (e.g., circle). When the input data have width or height only, the kernel moves across the one dimension as well. Here, convolution (\"1D convolution\") selects the points of the input data, which agree with the points of the kernel. The operation of convolution helps the convolutional neural network to detect specific characteristics of the input data, e.g., the form of a normal rhythm vs. its arrhythmia counterpart. However, the CNN has the issue of gradient vanishing: as it becomes deeper (its number of layers increases), the gradient of the loss with respect to the weight quickly becomes 0 [27; 28; 31]. The 1D CNN in this study consisted of the following layers (Figure 2): one 1D convolutional layer with 64 kernels of a \(3\times 1\) size and the activation function of the rectified linear unit; another 1D convolutional layer with 64 kernels of a \(3\times 1\) size and the activation function of the rectified linear unit; one dropout layer with a rate of 50%; one max pooling layer with a \(2\times 1\) size; one dense layer with 100 units of output space and the activation function of the rectified linear unit; and one dense layer with 2 units of output space and the activation function of softmax. Here, categorical cross-entropy was used as the loss function.