Automatic ECG quality scoring methodology: mimicking human annotators
Automatic ECG quality scoring methodology: mimicking human annotators
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DOI：
10.1088/0967-3334/33/9/1479
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发表时间：
2012-09-01
影响因子：
3.2
通讯作者：
Galeotti, Loriano
中科院分区：
文献类型：
--
作者：
Johannesen, Lars;Galeotti, Loriano
一种确定心电图质量的算法可以使没有经验的护士和护理人员记录足够诊断质量的心电图。之前，我们提出了一种算法，用于确定ECG记录是否具有可接受的质量，并参加了2011年PhysioNet挑战赛。在目前的工作中，我们提出了一种改进的两步算法，该算法首先拒绝具有宏观误差（信号缺失，大电压位移或饱和）的心电图，然后在连续尺度上量化噪声（基线，电力线或肌肉噪声）。改进算法的性能使用PhysioNet Challenge数据库（由人类对信号质量进行评级的1500个心电图）进行评估。我们在训练集上实现了92.3%的分类准确率，在测试集上实现了90.0%的分类准确率。改进后的算法能够检测出具有宏观误差的心电图，并为用户提供整体质量评分。这允许用户评估噪音的程度，并根据录音的目的决定是否可以接受。
An algorithm to determine the quality of electrocardiograms (ECGs) can enable inexperienced nurses and paramedics to record ECGs of sufficient diagnostic quality. Previously, we proposed an algorithm for determining if ECG recordings are of acceptable quality, which was entered in the PhysioNet Challenge 2011. In the present work, we propose an improved two-step algorithm, which first rejects ECGs with macroscopic errors (signal absent, large voltage shifts or saturation) and subsequently quantifies the noise (baseline, powerline or muscular noise) on a continuous scale. The performance of the improved algorithm was evaluated using the PhysioNet Challenge database (1500 ECGs rated by humans for signal quality). We achieved a classification accuracy of 92.3% on the training set and 90.0% on the test set. The improved algorithm is capable of detecting ECGs with macroscopic errors and giving the user a score of the overall quality. This allows the user to assess the degree of noise and decide if it is acceptable depending on the purpose of the recording.