Francisco Santos Neto's 2021 study validated a simple ECG algorithm for detecting ventricular tachycardia. The dataset includes 120 patient electrocardiograms from an electrophysiological study, with 82 recordings of VT and 38 of SVT-A. The study compared the new D12V16 algorithm's performance against the traditional Brugada algorithm.
Use Cases
- Validating ECG-based diagnostic algorithms based on QRS polarity features.
- Comparing the performance of different medical algorithms (e.g., D12V16 vs. Brugada) for VT detection.
- Training or benchmarking machine learning models for wide complex tachycardia classification.
- Studying the impact of physician expertise on algorithm accuracy in clinical settings.
Strengths
- Data is validated against a 'gold-standard' electrophysiological study.
- Includes performance metrics for two algorithms (e.g., D12V16 specificity of 85.1%, Brugada sensitivity of 87.2%).
- Analysis includes 120 patient ECG recordings with a defined breakdown (82 VT, 38 SVT-A).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count for the underlying dataset is unknown, which may limit suitability assessment.
- Data may reflect temporal or source bias inherent to paperswithcode.
Provenance
- Source
- Francisco Santos Neto
- Collection Method
- Prospectively obtained 12-lead electrocardiograms during electrophysiological studies, analyzed by six physicians.