Lateral connection convolutional neural networks for obstructive sleep apnea hypopnea classification.

Despite the successful operation of convolutional neural networks (CNN) with obstructive sleep apnea hypopnea (OSAHS) classification, the interpretability of these models is poor. The limited capacity to understand models hinders the comprehension of end-users, including sleep specialists. At the same time, these models need labeled data; however, this is a time-consuming, labor-intensive, and costly process. Furthermore, the presence of lateral connections plays a crucial role in the field of visual neurobiology. However, up until now, there has been a lack of research on CNN that incorporate lateral connections. In light of this, we introduce a novel CNN architecture called the lateral connection CNN (LCCNN), which integrates the semantic arrangement of neurons to classify OSAHS. The LCCNN consists of several layers, including a convolution layer for extracting local features, a lateral connection layer for detecting salient wave features, a competition layer for updating filters in an unsupervised manner, and a pooling layer. The competition layer ensures that adjacent filters in each convolution layer have similar weight distribution, thus realizing the semantic arrangement of neurons in the LCCNN. We evaluate the performance of the proposed model using the University College Dublin database (UCD) and the Physionet Challenge database (PCD). The results show that the proposed model achieves high total accuracies of 97.3% (with a kappa coefficient of 0.9) on UCD and 95.6% (with a kappa coefficient of 0.83) on PCD. This work can serve as a foundation for future research on unsupervised deep learning models.