Sleep Apnea Detection Based on Multi-Scale Residual Network

Aiming at the fact that traditional convolutional neural networks cannot effectively extract signal features in complex application scenarios, a sleep apnea (SA) detection method based on multi-scale residual networks is proposed. First, we analyze the physiological mechanism of SA, which uses the RR interval signals and R peak signals derived from the ECG signals as input. Then, a multi-scale residual network is used to extract the characteristics of the original signals in order to obtain sensitive characteristics from various angles. Because the residual structure is used in the model, the problem of model degradation can be avoided. Finally, a fully connected layer is introduced for SA detection. In order to overcome the impact of class imbalance, a focal loss function is introduced to replace the traditional cross-entropy loss function, which makes the model pay more attention to learning difficult samples in the training phase. Experimental results from the Apnea-ECG dataset show that the accuracy, sensitivity and specificity of the proposed multi-scale residual network are 86.0%, 84.1% and 87.1%, respectively. These results indicate that the proposed method not only achieves greater recognition accuracy than other methods, but it also effectively resolves the problem of low sensitivity caused by class imbalance.

Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

Weather Radar Image Superresolution Using a Nonlocal Residual Network

Accurate and high-resolution weather radar images reflecting detailed structure information of radar echo are vital for analysis and forecast of extreme weather. Typically, this is performed by using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated value regardless of the large-scale context feature of weather radar images. Inspired by the striking performance of the convolutional neural network (CNN) applied in feature extraction and nonlocal self-similarity of weather radar images, we proposed a nonlocal residual network (NLRN) on the basis of CNN. The proposed network mainly consists of several nonlocal residual blocks (NLRB), which combine short skip connection (SSC) and nonlocal operation to train the deep network and capture large-scale context information. In addition, long skip connection (LSC) added in the network avoids learning low-frequency information, making the network focus on high-level features. Extensive experiments of ×2 and ×4 super-resolution reconstruction demonstrate that NLRN achieves superior performance in terms of both quantitative evaluation metrics and visual quality, especially for the reconstruction of the edge and detailed information of the weather radar echo.

A Generative Adversarial Network Fused with Dual-Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

Aiming at the problem of insignificant target morphological features, inaccurate detection and unclear boundary of small-target regions, and multitarget boundary overlap in multitarget complex image segmentation, combining the image segmentation mechanism of generative adversarial network with the feature enhancement method of nonlocal attention, a generative adversarial network fused with attention mechanism (AM-GAN) is proposed. The generative network in the model is composed of residual network and nonlocal attention module, which use the feature extraction and multiscale fusion mechanism of residual network, as well as feature enhancement and global information fusion ability of nonlocal spatial-channel dual attention to enhance the target features in the detection area and improve the continuity and clarity of the segmentation boundary. The adversarial network is composed of fully convolutional networks, which penalizes the loss of information in small-target regions by judging the authenticity of prediction and label segmentation and improves the detection ability of the generative adversarial model for small targets and the accuracy of multitarget segmentation. AM-GAN can use the GAN’s inherent mechanism that reconstruct and repair high-resolution image, as well as the ability of nonlocal attention global receptive field to strengthen detail features, automatically learn to focus on target structures of different shapes and sizes, highlight salient features useful for specific tasks, reduce the loss of image detail features, improve the accuracy of small-target detection, and optimize the segmentation boundary of multitargets. Taking medical MRI abdominal image segmentation as a verification experiment, multitargets such as liver, left/right kidney, and spleen are selected for segmentation and abnormal tissue detection. In the case of small and unbalanced sample datasets, the class pixels’ accuracy reaches 87.37%, the intersection over union is 92.42%, and the average Dice coefficient is 93%. Compared with other methods in the experiment, the segmentation precision and accuracy are greatly improved. It shows that the proposed method has good applicability for solving typical multitarget image segmentation problems such as small-target feature detection, boundary overlap, and offset deformation.