Multi-task deep learning model

The multi-task deep learning model architecture illustrated in Fig. 8 consists of three main components: a shared part for both tasks, a specific part for SDB event detection, and another specific part for sleep–wake classification. The architecture was determined experimentally. The shared part was designed to learn common latent representations relevant to both tasks. It comprised two blocks, which were analogous to the feature extraction blocks employed in prior studies [14, 15]. Each block consisted of two layers of bidirectional gated recurrent units (GRU), a batch normalization layer, a max-pool layer, an activation layer utilizing the rectified linear unit (ReLU) activation function, and a dropout layer.

Multi-task model architecture; the parameters and output shape of each layer were presented together with the name of layer, the output shape of the layers which do not change the shape were not shown

The task-specific part for SDB event detection consisted of a feature extraction block and a classification block. The feature extraction block encompassed two layers of bidirectional GRUs, a batch normalization layer, an activation layer using the ReLU activation function, and a dropout layer. Subsequently, the classification block consisted of a dense-connected layer employing the ReLU activation function and another dense-connected layer utilizing the sigmoid activation function to generate the output.

Similarly, the task-specific part for sleep–wake classification included a feature extraction block combined with three subblocks. Each subblock was composed of a 2-dimensional convolution layer with ReLU activation, a batch normalization layer, a max-pool layer, and a dropout layer. Additionally, two reshape layers were incorporated at the beginning and end of the feature to adjust the shape for input and output. The classification block for sleep–wake classification mirrors that of event detection.