ECG_PLATFORM

ECG_PLATFORM is a complete framework designed for testing QRS detectors and ECG delineators on publicly available datasets.

In ECG_PLATFORM there are included the following algorithms for the detection of QRS complexes:

1. engzee segmenter - Andr ́e Louren ̧co, Hugo Silva, Paulo Leite, Renato Louren ̧co, and Ana Fred.Real Time Electrocardiogram Segmentation for Finger Based ECG Bio-metrics.
2. hamilton segmenter - Hamilton, Tompkins, W. J., "Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database", IEEE Trans. Biomed. Eng., BME-33, pp. 1158-1165, 1987.
3. WT delineator - Juan Pablo Martínez, Rute Almeida, Salvador Olmos, Member, IEEE, Ana Paula Rocha, and Pablo Laguna, Member, IEEE A Wavelet-Based ECG Delineator: Evaluation on Standard Databases
4. ECGPUWAVE - https://www.physionet.org/content/ecgpuwave/1.3.4/

Data sets on which the methods are tested:

1. cinc1 - https://physionet.org/pn3/challenge/2014/set-p/
2. cinc2 - https://physionet.org/pn3/challenge/2014/set-p2/
3. mitdb - https://archive.physionet.org/physiobank/database/mitdb/
4. mitdb (pwave annotations) - https://archive.physionet.org/physiobank/database/pwave/
5. qtdb - https://physionet.org/content/qtdb/1.0.0/
6. ludb - https://physionet.org/content/ludb/1.0.0/
7. telehealth - https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QTG0EP
8. BUT PDB - https://www.physionet.org/content/but-pdb/1.0.0/

Required Python packages: biosppy, wfdb, numpy, pandas, h5py, scipy, matplotlib, scikit-learn