Analysis of ECG Signals to Prediction of Ischemic Heart Disease Using Hybrid Neuro-fractal Analysis
Keywords:
Ischemic Heart Disease, Magneto-cardiograph, Electrocardiogram, Data mining, Bayesian neural networks, back-propagation neural networks Performance MeasuresAbstract
One of the leading causes of death is Ischemic Heart Disease (IHD). Rapid diagnosis and quick, correct diagnosis of IHD is crucial for lowering lifespan. The Magneto cardiogram (MCG) is a method for identifying myocardial electrophysiological activities. Because MCG eliminates skin-electrode contact, it does not have the drawbacks of the Electrocardiogram (ECG) approach. One of the most commonly used physiological signals, the ECG, provides vital health information. Gel-type ECG electrodes are commonly used in conventional ECG, but they are cumbersome for long-term or continuous ECG monitoring. Fractal analysis, statistical analysis, and neural network computation are used in this study to develop an ECG-based model that predicts how many times ischemia would occur based on ECG records. The benefit of the proposed technical above past studies is that fractal analysis results are used to develop a model that incorporates both clinical characteristics and signal qualities. To obtain a more precise model, statistical methods such as multivariate linear regression and binary logistic regression are employed. Meanwhile, MCG recording interpretation takes time and needs to be done by a professional. As a result, researchers suggest using machine learning to identify IHD individuals the Probabilistic Neural Network (PNN), Support Vector Machine (SVM), BayesianNeural Network (BNN), and Back-Propagation Neural Network (BPNN) were applied to develop classification models for identifying IHD patients. With our results, we can demonstrate that enhancing the previously indicated methodologies enhances prediction accuracy.
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