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.