心电图ECG信号自动检测特征提取方法研究进展

Overview of automatic detection method of ECG signal based on feature extraction

心电图特征参数特征提取技术是人体信号智能化检测领域研究热点之一。论文从差分阈值、模板匹配、小波变换、神经网络等多种特征提取法系统评述常见心电图特征参数自动化检测提取方法,阐述各种方法机理、主要研究应用方向及特点,总结分析指出各方法在不同应用场景下的优缺点。其中神经网络特征提取法准确性高、鲁棒性好,是心电图特征参数提取研究趋势及热点,后续可将神经网络深度学习、自学习与差分阈值、模板匹配、小波变换等特征提取方法相结合,实现更高要求的复杂心电图特征参数检测。

Electrocardiography(ECG) feature parameter extraction technology is one of the research hotspots in the field of human body signals intelligent detection. This paper systematically reviews the common automatic detection and extraction methods of ECG feature parameters, including differential threshold methods, template matching methods, wavelet transform methods, and neural network methods, and explains the mechanisms, characteristics and main application research directions of various methods, and analyzes the advantages and disadvantages of each method in different application scenarios. The neural network feature extraction method has high accuracy and good robustness, and it is the research trend and hot spot of ECG feature parameter extraction. In subsequent stages, deep learning and self-learning of neural network can be combined with differential threshold, template matching, wavelet transformation and other feature extraction methods to achieve higher requirements for complex ECG feature parameter detection.