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Computational Diagnostic Techniques for Electrocardiogram Signal Analysis
Liping Xie1, Zilong Li1, Yihan Zhou1
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Insights
Computer-aided techniques using electrocardiogram (ECG) signals offer fast, accurate detection of cardiovascular diseases (CVDs). These advanced methods, especially End-to-End models, simplify analysis and improve diagnostic accuracy for better heart health monitoring.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular diseases (CVDs) are the leading global cause of mortality.
- Early detection of CVDs is crucial for preventing cardiovascular death.
- Electrocardiogram (ECG) signals provide vital information about heart activity.
Purpose of the Study:
- To summarize the latest computational diagnostic techniques for estimating CVD conditions using ECG signals.
- To discuss the complete ECG signal analysis procedure, including preprocessing, feature engineering, and classification.
- To highlight the advantages of End-to-End models in ECG-based CVD diagnosis.
Main Methods:
- Review of computational diagnostic techniques for ECG signal analysis.
- Discussion of data preprocessing, feature engineering, and classification steps.
- Focus on End-to-End models integrating feature extraction and classification.
Main Results:
- Computer-aided techniques demonstrate success in fast and accurate CVD identification from ECG signals.
- End-to-End models simplify analysis, showing excellent accuracy and robustness.
- Portable devices offer new avenues for continuous cardiovascular monitoring.
Conclusions:
- Computational diagnostic techniques for ECG analysis hold significant potential for healthcare professionals.
- Widespread application of these techniques can benefit both patients and sub-healthy individuals in daily life.
- Advancements in AI-driven ECG analysis pave the way for improved cardiovascular health outcomes.
Abstract:
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
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