Modeling and Simulation of ECG Signal Segmentation for Coronary Artery Disease Detection
Implementation plan:
Step 1: Initially, we collect and load Coronary Artery Disease (CAD) Dataset.
Step 2: Next, we perform Signal Quality Evaluation
2.1: First, the Baseline wander Elimination process using Elliptic filter (Cauer filter),
2.2: Second, we implement Power-line interference Elimination process using Chebyshev type I filter
2.3: Third, we implement Electrode motion artifact Elimination process using Chebyshev type I filter
2.4: And we perform the normalization using Z-score normalization.
Step 3: Next, we perform ECG signal segmentation process using Improved Linear Regression based on feature extraction.
Step 4: Next, we implement the False Peak Elimination using amplitude axis adaptive threshold and morphological features by statistical evaluation
Step 5: Next, we implement the Hybrid Feature Extraction and Classification using Bidirectional Long short-term memory (BI-LSTM) and clustering the extracted features using K-means based on Horse Herd Optimization Algorithm (HOA).
step 6: Then, we implement Neural Basis expansion analysis for interpretable time series (N-BEATS).
Step 7: Finally, we plot performance for the following metrics:
7.1: Number of epochs vs. accuracy (%)
7.2: Number of epochs vs. Specificity (%)
7.3: Number of epochs vs. Sensitivity (%)
7.4: Number of epochs vs. F1-score (%)
7.5: Number of epochs vs. Confusion matrix
Dataset Link:
Artery Disease (CAD) – St.Petersburg Dataset (kaggle.com)
Software Requirement:
1. Development Tool: Python – 3.11.4 or above
2. Operating System: Windows 10 (64-bit)
Note:
1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the above plan satisfies your requirement please confirm with us.
4) We develop simulation based projects only, not in real time
