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Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet-based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.

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This paper presents a hybrid deep learning network submitted to the 6th Emotion Recognition in the Wild (EmotiW 2018) Grand Challenge [9], in the category of group-level emotion recognition. Advanced deep learning models trained individually on faces, scenes, skeletons and salient regions using visual attention mechanisms are fused to classify the emotion of a group of people in an image as positive, neutral or negative. Experimental results show that the proposed hybrid network achieves 78.98% and 68.08% classification accuracy on the validation and testing sets, respectively. These results outperform the baseline of 64% and 61%, and achieved the first place in the challenge.

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