An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion

Author:

Su Shi123,Zhu Zhihong3,Wan Shu34,Sheng Fangqing5,Xiong Tianyi1,Shen Shanshan1,Hou Yu1,Liu Cuihong1,Li Yijin1,Sun Xiaolin1,Huang Jie1

Affiliation:

1. School of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China

2. Innovative Research Laboratory of Nanjing Xi-Jing Advanced Materials Technology Ltd., Nanjing 211101, China

3. 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

4. Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology, School of Optoelectronics Engineering, Chongqing University, Chongqing 400044, China

5. School of Economics and Management, Nanjing Vocational University of Industry Technology, Nanjing 210023, China

Abstract

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.

Funder

Start-up Fund for New Talented Researchers of Nanjing Vocational University of Industry Technology

General Project of Philosophy and Social Science Research in Colleges of Jiangsu Province

Natural Science Foundation of the Jiangsu higher Education Institutions of China

High-level Training Project for Professional-leader Teachers of Higher Vocational Colleges in Jiangsu Province

‘Qing Lan Project’ for Excellent Young Core Teachers of Jiangsu Province

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference36 articles.

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2. Graphene-Based Sensors for Human Health Monitoring;Huang;Front. Chem.,2019

3. The Genesis of the Electrocardiogram;Katz;Physiol. Rev.,1947

4. Development and Application of Artificial Intelligence;Li;J. Beijing Univ. Technol.,2020

5. ECG-based machine-learning algorithms for heartbeat classification;Aziz;Sci. Rep.,2021

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1. Bimodal Framework for Cardiac Arrhythmia Analysis using Deep Learning;2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC);2024-05-02

2. RETRACTED: Enhanced X-wave recognition in ECG signal using max–min thresholds and detection of QRS complex;Journal of Intelligent & Fuzzy Systems;2024-04-25

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