Dual Attention Convolutional Neural Network Based on Adaptive Parametric ReLU for Denoising ECG Signals with Strong Noise.

Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoising is therefore necessary before further analysis. Deep learning-related methods have been applied to image processing and other domains with great success but are rarely used for denoising ECG signals. This paper proposes an effective and simple model of encoder-decoder structure for denoising ECG signals (APR-CNN). Specifically, Adaptive Parametric ReLU (APReLU) and Dual Attention Module (DAM) are introduced in the model. Rectified Linear Unit (ReLU) is replaced with the APReLU for better negative information retainment. The DAM is an attention-based module consisting of a channel attention module and spatial attention module, through which the inter-spatial and inter-channel relationship of the input data are exploited. We tested our model on the MIT-BIH dataset, and the results show that the APR-CNN can handle ECG signals with a different signal-to-noise ratio (SNR). The comparative experiment proves our model is better than other deep learning and traditional methods.Clinical Relevance- This paper proposed a method capable of denoising ECG signals with strong noise to alleviate difficulties for further medical analysis.

Assessment of the green development level for participating countries in the Belt and Road initiative.

Green Belt and Road development has gradually become a global consensus, and the quantitative assessment of the green development level constitutes the basis for building a green Belt and Road high-quality development path in practice. In this paper, the Belt and Road Green development index (BRGI) was proposed in three dimensions, i.e., green nature, green economy and green society, to evaluate the green development spatial and temporal characteristics of the 80 participating countries in the Belt and Road Initiative from 2010 to 2018, and based on the quadrant method, green development cooperation model was established. The results showed: (1) In 2018,the average BRGI of participating countries is 54.38, and more than half of the countries have not reached the average level; From a regional perspective, the green development level in Europe is the highest, followed by Northeast Asia and Southeast Asia, and it is the lowest in South Asia and Africa. (2) At the considered time scale, the green development level in the Belt and Road participation countries has been increased from 2010 to 2018. (3) The green Belt and Road development cooperation modes can be divided into the all-round high-level energy attraction cooperation model, systematic win-win cooperation model for the whole field, three-dimensional refined empowerment cooperation model and multilevel high-trust cooperation. According to the different cooperation modes, the study also provides policy recommendations to promote for green development.