A smart system for accurate detection and classification of cardio vascular diseases using advanced analysis

Cardiovascular diseases (CVDs) are the leading cause of global mortality, highlighting the need for accurate and early diagnosis to enhance patient outcomes. However, effectively detecting and classifying CVDs using ECG signals remains a critical challenge. Hence, a novel Adaptive Temporal Wavelet-Kalman Fusion Transformer Network is proposed for enhanced CVD diagnosis. Initially, existing models struggle to capture nonlinear ventricular repolarization dynamics and subtle T-wave heterogeneity, leading to incomplete feature extraction. So, a novel Temporal Sparse Wavelet-Fourier Attention Transformer integrates Sparse Wavelet-Fourier Transform Decomposition to extract transient ECG variations, enhancing feature extraction. In contrast, Temporal Convolutional Transformers (TCTs) captures temporal dependencies for improved detection. Besides, existing ECG-based models fail to distinguish autonomic dysfunction (AD)-induced variations from pathological abnormalities, leading to misclassifications, and delayed diagnoses in CVD detection. Thus, an Adaptive Temporal Kalman Deep Fusion Forest Network integrates Adaptive Hybrid Empirical Kalman Decomposition to filter autonomic noise. At the same time, the Temporal Residual Gated Fusion Forest Network extracts spatial features, enhancing classification robustness. Further, the framework is validated using Stratified K-Fold Cross-Validation to ensure fairness and minimize bias, with experimental results demonstrating high accuracy, precision, and low MSE, leading to improved detection and classification of CVD. • Objective: Cardiovascular disease detection and classification using ECG signals • Proposed: A novel Adaptive Temporal Wavelet-Kalman Fusion Transformer Network • Temporal Sparse Wavelet-Fourier Attention Transformer for ECG feature extraction • Adaptive Temporal Kalman Deep Fusion Forest Network accurately classifies CVD