Introduction

The China Physiological Signal Challenge 2019 (CPSC 2019) aims to encourage the development of algorithms for challenging QRS detection and heart rate (HR) estimation from short-term single-lead ECG recordings usually with low signal quality and/or abnormal rhythm waveforms.

ECG signal provides an important role in non-invasively monitoring and clinical diagnosis for cardiovascular disease (CVD). Detection of QRS complex is an essential step for ECG signal processing, and can benefit the following HR calculation and abnormal situation analysis. Although detection methods of QRS complex have been severely tracked throughout the last several decades, accurate QRS location and HR estimation are still challenging in noisy signal episode or abnormal rhythm waveforms, especially when the ECG recordings are from the wearable dynamic ECG acquisition. It is true that many of the developed QRS detection algorithms can achieve high accuracy (over 99% in sensitivity and positive predictivity) when tested over the standard ECG databases such as MIT-BIH Arrhythmia Database or AHA Database [1]. However, these algorithms may not be able to perform well when used in the daily life environment that will cause severe noises and significantly reduce the signal quality. A recent study confirmed that none of the common QRS algorithms can obtain 80% detection accuracy when tested in a common dynamic noisy ECG database [2]. Thus, in this challenge, we provide a new ECG database containing noisy ECG episodes and/or signals with different arrhythmia patterns, encouraging participants to develop more efficient and robust algorithms QRS detection and HR estimation. In addition, it is worth to note that, although HR can be calculated from the detection results of QRS complexes, HR can be still estimated without QRS detection step [3,4].

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 [5,6] 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.

Reference

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2. Liu, F.F.; Wei, S.S.; Li, Y.B.; Jiang, X.E.; Zhang, Z.M.; Liu, C.Y., Performance analysis of ten common qrs detectors on different ecg application cases. Journal of Healthcare Engineering 2018, 2018, ID 9050812.
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4. J. Gieraltowski, K. Ciuchcinski, I. Grzegorczyk, K. Kosna, M. Solinski, P.Podziemski, RS slope detection algorithm for extraction of heart rate from noisy, multimodal recordings, Physiological Measurement, 36 (2015) 1743-1761.
5. 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.
6. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm, Biomedical Engineering, IEEE Transactions on, (1985) 230-236.
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