CaP: Cardiovascular Disease Prediction using a Delta Layer based Center Vector Activation-centric DCNN

Abstract

Cardiac disease stands as a primary contributor to mortality, representing a prevalent category of chronic and life-threatening conditions. Therefore, early detection is imperative. While existing research has sought to predict heart disease (HD) through Electrocardiogram (ECG) signals, there remains room for enhancement. This study introduces a novel approach for early HD detection based on the Delta Layer with Center Vector Activation-centric Deep Convolutional Neural Network (DLCVA-DCNN) within its research framework, namely: CaP. Initially, the input ECG signals undergo preprocessing using a Weighted Covariance Kalman Filter (WCKF) to eliminate noise. Subsequently, the preprocessed data is bifurcated: one branch transforms it into a binary image, while the other decomposes the signal to identify peak segments. The decomposition employs the Bivariate Ensemble Empirical Mode Decomposition (BEEMD) method, and the Pan-Tompkins Algorithm (PTA) is applied to ascertain the highest-frequency segments. The coupling information is then extracted from these peaks. Simultaneously, depth features are extracted from the binary image. The Linear Approximate Functional Walrus Optimization Algorithm (LAFWOA) is employed to select pertinent features from the coupling and depth features. These selected features are input into the DLCVA-DCNN classifier to discriminate disease and standard signals. The experimental analysis compares the proposed methodology with conventional frameworks based on performance metrics, revealing that the proposed approach achieves higher accuracy than existing techniques.