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Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method
M Mohamed Suhail1, T Abdul Razak1
1Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India.
Insights
This study introduces an automated framework for detecting heart disease using electrocardiogram (ECG) data and nonlinear analysis. The developed model achieves high accuracy, improving early diagnosis of cardiovascular conditions.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Cardiac disease is a leading global cause of mortality.
- Early diagnosis of cardiovascular problems is crucial for prevention.
- Electrocardiogram (ECG) is a key diagnostic tool for heart conditions.
Purpose of the Study:
- To develop an automated framework for heart disease detection using ECG analysis.
- To integrate multi-field extraction and nonlinear analysis for improved diagnosis.
- To create a model for future diagnosis of cardiovascular disease via ECG and symptom-based detection.
Main Methods:
- Utilized Discrete Wavelet Transform (DWT) for ECG signal preprocessing to remove noise.
- Employed Nonlinear Vector Decomposed Neural Network (NVDNN) for heart disease prediction.
- Trained the neural network with thirteen clinical features for classification.
Main Results:
- The system achieved high performance metrics: 92.0% sensitivity, 89.33% specificity, and 90.67% accuracy.
- Modules were successfully implemented, trained, and tested on UCI and PhysioNet data repositories.
- The approach demonstrated effectiveness in identifying cardiac illness through ECG categorization.
Conclusions:
- The proposed framework effectively identifies complex nonlinear correlations in ECG data.
- This approach enhances ECG classification accuracy for more precise cardiac disease diagnosis.
- The method offers superior accuracy in ECG categorization for identifying cardiac illness compared to other techniques.
Objectives:
Cardiac disease is the leading cause of death worldwide. If a proper diagnosis is made early, cardiovascular problems can be prevented. The ECG test is a diagnostic method used on the screen for heart disease. Based on a combination of multi-field extraction and nonlinear analysis of ECG data, this paper presents a framework for automated detection of heart disease. The main aim of this study is to develop a model for future diagnosis of cardiac vascular disease using ECG analysis and symptom-based detection.
Methods:
Discrete wavelet transform and Nonlinear Vector Decomposed Neural Network methods are used to predict Cardiac disease. Here is the discrete wavelet transform used for preprocessing to remove unwanted noise or artifacts. The neural network was fed with thirteen clinical features as input which was then trained using a non-linear vector decomposition of the presence or absence of heart disease.
Results:
The modules were implemented, trained, and tested using UCI and Physio net data repositories. The sensitivity, specificity and accuracy of this research work are 92.0%, 89.33% and 90.67% CONCLUSIONS: The proposed approach can discover complex non-linear correlations between dependent and independent variables without requiring traditional statistical training. The suggested approach improves ECG classification accuracy, allowing for more accurate cardiac disease diagnosis. The accuracy of ECG categorization in identifying cardiac illness is far greater than these other approaches.
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