Algorithm of Left Bundle Branch Block Diagnosis Based on ELM
Wang Zhiqiong1, Wu Chengyang1, Xin Junchang2, Zhao Yue1*, Li Xiang3*
1(Sino-Dutch Biomedical and Information Engineering School of Northeastern University, Shenyang 110169, China) 2(College of Information Science and Engineering of Northeastern University, Shenyang 110004, China) 3(The Second Hospital of Dalian Medical University, Dalian 116027, China)
Abstract：As a common clinical arrhythmia, left bundle branch block is a signal of left ventricular systolic function decreased and mortality increased in patients, machine learning algorithm aided diagnosis of the disease will play a positive role in detection and diagnosis. Currently, left bundle branch block automatic identification mode is still using support vector machines and other traditional machine learning algorithms for training and testing, these traditional neural network algorithms prone to local optimal solution, which is not suitable to classified LBBB. Herein, this paper proposed an algorithm about automatic diagnosis of left bundle branch block based on ELM. Firstly, the ECG signal was preprocessed, including the removal of baseline drift, high-frequency noise and power-line interference; then, we created the model by features of LBBB such as the length of QRS after the location of QRS-T wave was determined. Finally, we provided the LBBB diagnosis algorithm based on ELM. Additionally, we tested 5000 groups of data in MIT_BIH. Results showed the algorithm was effective in noise removal and wave extraction. ELM was 88.5% that is shorter than SVM in training time, and ELM had improvement of 2.4%, 5.4%, 1.2%, 3.6%, 2% in time, accuracy, sensitivity, specificity, FP ratio and FN ratio respectively. Accordingly, ELM had more advantages in LBBB diagnosis.
王之琼, 吴承暘, 信俊昌, 赵 越, 李 响. 基于极限学习机的左束支传导阻滞辅助诊断研究[J]. 中国生物医学工程学报, 2017, 36(3): 293-299.
Wang Zhiqiong, Wu Chengyang, Xin Junchang, Zhao Yue, Li Xiang. Algorithm of Left Bundle Branch Block Diagnosis Based on ELM. Chinese Journal of Biomedical Engineering, 2017, 36(3): 293-299.
