Malik John, Soliman Elsayed Z, Wu Hau-Tieng
Department of Mathematics, Duke University, Durham, NC, USA.
Epidemiological Cardiology Research Center (EPICARE), Department of Epidemiology and Prevention, and Department of Medicine, Cardiology Section, Wake Forest School of Medicine, Winston-Salem, NC, USA.
J Electrocardiol. 2020 May-Jun;60:165-171. doi: 10.1016/j.jelectrocard.2020.02.016. Epub 2020 Feb 27.
Accurate detection of QRS complexes during mobile, ultra-long-term ECG monitoring is challenged by instances of high heart rate, dramatic and persistent changes in signal amplitude, and intermittent deformations in signal quality that arise due to subject motion, background noise, and misplacement of the ECG electrodes.
We propose a revised QRS detection algorithm which addresses the above-mentioned challenges.
Our proposed algorithm is based on a state-of-the-art algorithm after applying two key modifications. The first modification is implementing local estimates for the amplitude of the signal. The second modification is a mechanism by which the algorithm becomes adaptive to changes in heart rate. We validated our proposed algorithm against the state-of-the-art algorithm using short-term ECG recordings from eleven annotated databases available at Physionet, as well as four ultra-long-term (14-day) ECG recordings which were visually annotated at a central ECG core laboratory. On the database of ultra-long-term ECG recordings, our proposed algorithm showed a sensitivity of 99.90% and a positive predictive value of 99.73%. Meanwhile, the state-of-the-art QRS detection algorithm achieved a sensitivity of 99.30% and a positive predictive value of 99.68% on the same database. The numerical efficiency of our new algorithm was evident, as a 14-day recording sampled at 200 Hz was analyzed in approximately 157 s.
We developed a new QRS detection algorithm. The efficiency and accuracy of our algorithm makes it a good fit for mobile health applications, ultra-long-term and pathological ECG recordings, and the batch processing of large ECG databases.
在移动、超长期心电图监测过程中，准确检测QRS波群面临诸多挑战，包括心率过高、信号幅度急剧且持续变化，以及由于受检者运动、背景噪声和心电图电极放置不当导致的信号质量间歇性变形。
我们提出一种改进的QRS检测算法，以应对上述挑战。
我们提出的算法基于一种先进算法，并进行了两项关键修改。第一项修改是对信号幅度进行局部估计。第二项修改是使算法能够适应心率变化的机制。我们使用Physionet提供的11个带注释数据库中的短期心电图记录，以及在中央心电图核心实验室进行视觉注释的4个超长期（14天）心电图记录，将我们提出的算法与先进算法进行了验证。在超长期心电图记录数据库上，我们提出的算法灵敏度为99.90%，阳性预测值为99.73%。同时，先进的QRS检测算法在同一数据库上的灵敏度为99.30%，阳性预测值为99.68%。我们新算法的数值效率显著，对以200Hz采样的14天记录进行分析大约需要157秒。
我们开发了一种新的QRS检测算法。我们算法的效率和准确性使其非常适合移动健康应用、超长期和病理性心电图记录，以及大型心电图数据库的批量处理。
