3.2.2. Channel Spatial Attention Mechanism

To better focus on abnormal ECG data, a channel spatial attention mechanism is added to the model, which can focus on channel information and spatial information at the same time, compared with the “Squeeze-and-Excitation” (SE) module that only focuses on channels [31] which has better performance. The structure is shown in Figure 4, which can combine channel information and spatial information at the same time.

Channel convolution attention mechanism diagram (top (a): channel attention mechanism; bottom (b): spatial attention mechanism).

The channel spatial attention mechanism includes two submodules, the channel attention mechanism (Figure 4(a)) and the spatial attention mechanism (Figure 4(b)). The channel attention mechanism obtains the channel attention map M_c through the selection of the channel, and in the other spatial attention mechanism to the important part of the feature of the channel, the spatial attention map M_s is obtained. The input feature F passes through these two parts to obtain the detailed feature F^″. These two steps are represented by equations (2) and (3), respectively:

Figure 4(a) shows how the channel attention mechanism works, and its ability to channel the selection of input features allows the model to focus more on channels that are useful for the task. The parameters of this module were obtained by calculating the global average pooling and the global maximum pooling information about input features, followed by merging these two parts of information, in this process both share the same fully connected network, and finally the spatial attention weights are compressed into 0-1 using the sigmoid activation function. This process can be shown as

Figure 4(b) shows how the spatial attention mechanism works, which can reduce the interference of other nonimportant information on the same channel to the task and improve the accuracy of the model. Features that underwent a global maximum pooling and global average pooling of features output by the channel attention mechanism were convolved, using the sigmoid activation function to compress spatial attention weights to 0-1. This process can be shown as

The channel attention mechanism focuses on the channels that contribute more to the ECG signal. The spatial attention mechanism assigns greater weight to more important information in different time periods of the ECG signal. The channel attention mechanism is a global application, and the spatial attention mechanism is local to the feature which plays an important role. Literature [28] shows that sequential placement has better performance than parallel placement, and the performance of channel priority is higher than spatial priority. Therefore, the attention mechanism is placed between the first convolution layer and the pooling layer and before the last pooling layer for the two streams, respectively. And the channel attention mechanism is prior to the spatial attention mechanism.