Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.
Improving the diffusion kinetics of sodium ions within TiO2 and its intrinsic electronic conductivity is indispensable to enhance the rate capability and long cyclic stability of TiO2 anodes for sodium‐ion batteries. Although single‐heteroatom doping into TiO2 has been widely investigated, a comprehensive understanding of the effects of dual‐heteroatoms doping on the sodium storage performance of TiO2 is still lacking. Herein, nitrogen and sulfur dual‐doping is proposed to achieve a high doping concentration for anatase TiO2 hollow spheres. Experimental data and theoretical calculations reveal that N doping can efficiently narrow the bandgap of TiO2, while S doping is effective in facilitating Na+ diffusion within TiO2. Thus N and S codoped TiO2 shows remarkably boosted electronic conductivity, as well as accelerated sodium ion transfer kinetics owing to the synergistic effect of different doping heteroatoms, which leads to exceptional rate performance (307.5 and 156.4 mAh g−1 at 33.5 and 5025 mA g−1, respectively), and extraordinary cycling stability (90.5% retention over 2400 cycles at 3350 mA g−1). The greatly improved electrochemical performance emphasizes the importance of defects engineering in the rational design of advanced battery materials.
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