Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.