Challenge Data

Training data consists of 2,000 single-lead ECG recordings collected from patients with cardiovascular disease (CVD), each of the recording last for 10 s. Test set contains similar ECG recordings of same lengths, which is unavailable to public and will remain private for the purpose of scoring for the duration of the Challenge and for some period afterwards. ECG recordings were obtained from multiple sources using a variety of instrumentation, although in all cases they are presented as 500 Hz sample rate here. All recordings were provided in MATLAB format (each including two .mat file: one is ECG data and another one is the corresponding QRS annotation file). Pan &Tompkins (P&T) algorithm [1,2] is also provided as benchmark or comparable algorithm.

Although QRS detection and HR estimation are widely studied by lots of researchers for many years, accurate detection is still really challenging in this Challenge due to the QRS amplitude variation, QRS morphological variation, and occurrence of intense variability in the intervals between beats, different arrhythmias, as well as noises. Figure 1 shows the examples of ECG waveforms in training data.



Figure 1. Examples of the represented ECG waveforms. Red circles denote the reference QRS locations and black ones denote the detected results by the P&T algorithm. The challenges for accurate QRS locations are from: A) intense variability, B) premature ventricular contraction (PVC) and C) ventricular tachycardia.

[2019-6-21] To solve the confusion caused by the old version of evaluation algorithm while dealing with the boundary problem, we fixed and resubmit all scoring code. Meanwhile, some labeling error in training set was fixed and resubmit as Training set!

If you use the Challenge data for paper publication, please cite this paper for Challenge data description:
H. X. Gao, C. Y. Liu, X. Y. Wang, L. N. Zhao, Q. Shen, E. Y. K. Ng, and J. Q. Li. An Open-Access ECG Database for Algorithm Evaluation of QRS Detection and Heart Rate Estimation. Journal of Medical Imaging and Health Informatics, 2019, 9(9): 1853–1858.

Reference
1.   P.S. Hamilton, W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database, Biomedical Engineering, IEEE
      Transactions on, (1986) 1157-1165.
2.   J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, Biomedical Engineering, IEEE Transactions on, (1985) 230-236.