Effects Analysis on the Immunity of Classification Algorithm of
Electrocardiograph Based on Neural Network
WANG Hao1, TANG Qiaohong1, TANG Na2, HAO Ye1, LI Shu1, MENG Xiangfeng1, LI Jiage1
1. Institute for Medical Device Control, National Institutes for Food and Drug Control, Beijing 102629, China;
2. College of Bioengineering, Chongqing University, Chongqing 400044, China
Abstract：Objective To study factors that impacts immunity of artificial intelligence-enabled electrocardiograph classification
algorithms. Methods By using the open electrocardiograph database and artificial intelligence-enabled electrocardiograph
classification algorithm model, the noise measured in the laboratory was introduced into the algorithm training and testing, and
the sample size of the training set was changed at the same time to observe the changing trend of the algorithm test results.
Results When the test set was overlapped with noise, the overall accuracy of the algorithm classification was decreased more than
3%. When the same type of noise was added to the training set and the test set, the overall accuracy of the algorithm classification
decreased by no more than 0.5%. For different types of heart beats, the sample size of training set had different influence trends on
the classification accuracy of the algorithm. Conclusion Necessary noise can be introduced to the electrocardiograph classification
algorithm in the training stage to enhance the algorithm’s immunity, but at the same time, attention should be paid to the differences
between different classifications. The expansion of the training set does not necessarily improve the immunity of the algorithm.
王浩1，唐桥虹1，唐娜2，郝烨1，李澍1，孟祥峰1，李佳戈1. 基于神经网络的心电分类算法
抗扰性影响分析[J]. 中国医疗设备, 2023, 38(3): 61-65.
WANG Hao1, TANG Qiaohong1, TANG Na2, HAO Ye1, LI Shu1, MENG Xiangfeng1, LI Jiage1. Effects Analysis on the Immunity of Classification Algorithm of
Electrocardiograph Based on Neural Network. China Medical Devices, 2023, 38(3): 61-65.
