Electrocardiogram classification method based on deep learning model

The invention discloses an electrocardiogram classification method based on a deep learning model, and the method is characterized in that the method comprises the steps: data obtaining, data processing, model construction, algorithm optimization, and model training. A technical problem to be solved in the invention is to carry out the discrimination of arrhythmia through electrocardiogram data, to provide assistance and reference for a doctor, and to solve problems that doctors are not sufficient in some places and the wrong diagnosis and diagnosis leakage rate are higher. According to the embodiment of the invention, the invention has the following beneficial effects that the method employs the deep learning method, and achieves the discrimination of arrhythmia in the electrocardiogram information through the building of a large-scale convolution neural network. Compared with the conventional model, the method saves the cost, and is higher in accuracy. 本发明公开种基于深度学习模型的心电图分类方法，其特征在于：包括数据获取、数据处理、模型构建、优化算法、模型训练