ECG Signal Classification Based on Fusion of Hybrid CNN and Wavelet Features by D-S Evidence Theory.

At present, cardiovascular disease is regarded as one of the dangerous diseases that threaten human life. The morbidity and lethality caused by cardiovascular disease are constantly increasing every year. In this paper, we propose a two-stream style operation to handle the electrocardiogram (ECG) classification: one for time-domain features and another for frequency-domain features. For the time-domain features, convolutional neural networks (CNN) are constructed for feature learning and classification of ECG signals. For the frequency-domain features, support vector regression (SVR) machines are designed to perform the regression prediction on each signal. Finally, the D-S evidence theory is adopted to perform the decision fusion strategy on the time-domain and frequency-domain classification results. We confirm a recognition performance of 99.64% from the experiment result for the D-S evidence theory recognition system upon the MIT-BIH arrhythmia database. The analysis of various methods of ECG classification shows that the model delivers superior performance promotion across all these scenarios.

Coupling anammox with denitrification in a full-scale combined biological nitrogen removal process for swine wastewater treatment.

In order to enhance nitrogen removal through anammox process in the full-scale swine wastewater treatment plant, an innovative regulation strategy of nitrate-based carbon dosage and intermittent aeration was developed to apply the combined biological nitrogen removal process in a full scale anaerobic-anoxic-oxic (A2/O) system. TN removal efficiency reached at 65.5 ± 6.0% in Phase 1 with decreasing external carbon dosage in influent due to the reduction of return nitrate concentration, and it increased to 83.5 ± 6.7% when intermittent aeration was adopted in oxic zone and external carbon source was stopped adding into influent in Phase 2. As a result, the energy consumption for the swine wastewater treatment decreased from 1.93 to 0.9 kW h/m3 and 4.18 to 2.57 kW h/kg N, respectively. Microbial community analysis revealed that the average abundances of Candidatus Brocadia increased from 0.76% to 2.43% and removal of TN through anammox increased from 39% to 77%.