"ECG身份识别研究：多尺度神经网络应用探索"

The research on multi-scale structural neural network for ECG identification presented in the document aims to address the increasing demand for more secure and reliable identity recognition systems in the era of rapid Internet development. ECG signals, as a form of biometric data, offer unique advantages such as in vivo detection, non-invasive validation without requiring explicit user actions, and the additional benefit of containing valuable cardiac health information. The study delves into the development and optimization of a multi-scale structural neural network for ECG identification, leveraging the intricate patterns and features captured by ECG signals for accurate and efficient recognition of individuals. By analyzing ECG data at different scales, the neural network is able to extract a rich set of features that contribute to enhanced identification performance. Through rigorous experimentation and evaluation, the effectiveness and robustness of the proposed multi-scale structural neural network are demonstrated. The results show that the network achieves high accuracy and reliability in ECG identification tasks, showcasing its potential as a viable solution for enhancing information security through biometric authentication. Overall, the research contributes to the advancement of identity recognition systems by harnessing the unique characteristics of ECG signals and leveraging the power of neural networks to achieve superior performance. The findings hold significant implications for various applications requiring secure and efficient user authentication, and pave the way for further advancements in the field of biometric technology.