An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion
Shi Su,
Zhihong Zhu,
Shu Wan,
Fangqing Sheng,
Tianyi Xiong,
Shanshan Shen,
Yu Hou,
Cuihong Liu,
Yijin Li,
Xiaolin Sun,
Jie Huang
Affiliations
Shi Su
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Zhihong Zhu
SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
Shu Wan
SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Collaborative Innovation, Center for Micro/Nano Fabrication, Device and System, Southeast University, Nanjing 210096, China
Fangqing Sheng
School of Economics and Management, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Tianyi Xiong
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Shanshan Shen
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Yu Hou
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Cuihong Liu
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Yijin Li
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Xiaolin Sun
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Jie Huang
School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system’s classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.
