A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection

Keyphrases

• Abrupt Change 25%
• Automatic Delineation 25%
• Automatic Learning 25%
• Cardiovascular Disease 25%
• Change Point Analysis 100%
• Change Point Detection 100%
• Change Sequence 25%
• Constraint Graph 100%
• Detection Accuracy 25%
• Detection Model 75%
• Detection Problem 25%
• Detection Rate 50%
• Detection Task 25%
• Electrocardiogram Signal 100%
• Fiducial Points 50%
• Graph Anomaly Detection 25%
• Graph Learning Algorithm 25%
• Graph Search 100%
• Graph Structure 25%
• Graph-constrained 50%
• Graphical Models 25%
• Learning from Constraints 25%
• MIT-BIH Arrhythmia Database 25%
• Non-invasive Device 25%
• Optimal Graph 25%
• Overall Sensitivity 50%
• Positive Predictivity 50%
• Preprocessing Techniques 25%
• Prior Biological Knowledge 25%
• QRS Detection 100%
• R-peak 25%
• R-peak Detection 25%
• Signal Analysis 25%
• Sparsity 25%

Computer Science

• Analysis Signal 25%
• Biological Knowledge 25%
• Constraint Graph 100%
• Detection Accuracy 25%
• Error Detection 50%
• Fiducial Point 50%
• Graphical Model 25%
• Learning Algorithm 25%
• Preprocessing Step 25%
• Search Algorithm 100%
• Sparsity 25%