面向临床心电图分析的深层学习算法
摘要
As one of the classical applications of pattern recognition research, electrocardiogram (ECG) classi- fication has important application values for wearable ECG devices and "cloud" service platforms. In this paper, first of all, the complexity of the ECG classi cation model for clinical application is illustrated. Consequently, the approximation ability of a nonlinear function in an existing feature extraction and classi cation algorithm is analyzed, and deep learning is employed for ECG classi cation. Then, lead convolutional neural networks (LCNN) is presented considering the special two-dimensional structure of multi-lead ECG, in which "translating starting point" and "adding noise" are two of the main strategies to increase the training sample. Tests conducted using more than 150,000 ECG records show that the proposed method has an accuracy of 83.66% and 0.9086 AUC. Finally, the classi cation model is implemented on a mobile terminal, where its real-time analysis performance is shown to meet application requirements.