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Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG
Muhammad Adam1, Shu Lih Oh1, Vidya K Sudarshan1
1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Insights
This study introduces a novel method using discrete wavelet transform and nonlinear features for automated cardiovascular disease detection from ECG signals. The approach significantly improves diagnostic accuracy, aiding early intervention and increasing survival rates.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality.
- Early detection and treatment are crucial for reducing CVD mortality rates.
- Manual analysis of electrocardiogram (ECG) signals for CVD diagnosis is time-consuming and prone to error.
Purpose of the Study:
- To develop an automated method for characterizing CVDs using ECG signals.
- To overcome the limitations of manual ECG analysis through advanced signal processing techniques.
Main Methods:
- A novel discrete wavelet transform (DWT) method combined with nonlinear features was employed.
- ECG signals from normal subjects and patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and myocardial infarction (MI) were analyzed.
- Four nonlinear features (fuzzy entropy, sample entropy, fractal dimension, signal energy) were extracted from DWT coefficients.
- Sequential forward selection (SFS) and ReliefF ranking were used to select the most relevant features.
Main Results:
- The proposed methodology achieved a maximum classification accuracy of 99.27%.
- High sensitivity (99.74%) and specificity (98.08%) were obtained using a K-nearest neighbor (kNN) classifier with 15 selected features.
- The automated system demonstrated superior performance in classifying different cardiac conditions.
Conclusions:
- The developed DWT-based method with nonlinear features offers a robust and accurate approach for automated CVD detection.
- This methodology can assist clinical staff in making faster and more accurate diagnoses.
- Early and precise diagnosis facilitated by this system can significantly improve patient survival rates.
Abstract:
Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
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