Department of Electrical Engineering, KU Leuven, B-3001 Leuven, Belgium.
imec, B-3001 Leuven, Belgium.
Comput Methods Programs Biomed. 2019 Dec;182:105050. doi: 10.1016/j.cmpb.2019.105050. Epub 2019 Aug 24.
The presence of noise sources could reduce the diagnostic capability of the ECG signal and result in inappropriate treatment decisions. To mitigate this problem, automated algorithms to detect artefacts and quantify the quality of the recorded signal are needed. In this study we present an automated method for the detection of artefacts and quantification of the signal quality. The suggested methodology extracts descriptive features from the autocorrelation function and feeds these to a RUSBoost classifier. The posterior probability of the clean class is used to create a continuous signal quality assessment index. Firstly, the robustness of the proposed algorithm is investigated and secondly, the novel signal quality assessment index is evaluated.
Data were used from three different studies: a Sleep study, the PhysioNet 2017 Challenge and a Stress study. Binary labels, clean or contaminated, were available from different annotators with experience in ECG analysis. Two types of realistic ECG noise from the MIT-BIH Noise Stress Test Database (NSTDB) were added to the Sleep study to test the quality index. Firstly, the model was trained on the Sleep dataset and subsequently tested on a subset of the other two datasets. Secondly, all recording conditions were taken into account by training the model on a subset derived from the three datasets. Lastly, the posterior probabilities of the model for the different levels of agreement between the annotators were compared.
AUC values between 0.988 and 1.000 were obtained when training the model on the Sleep dataset. These results were further improved when training on the three datasets and thus taking all recording conditions into account. A Pearson correlation coefficient of 0.8131 was observed between the score of the clean class and the level of agreement. Additionally, significant quality decreases per noise level for both types of added noise were observed.
The main novelty of this study is the new approach to ECG signal quality assessment based on the posterior clean class probability of the classifier.
噪声源的存在会降低心电图信号的诊断能力，并导致治疗决策不当。为了解决这个问题，需要使用自动算法来检测伪迹并量化记录信号的质量。在这项研究中，我们提出了一种用于检测伪迹和量化信号质量的自动方法。所提出的方法从自相关函数中提取描述性特征，并将这些特征馈送到 RUSBoost 分类器中。干净类的后验概率用于创建连续的信号质量评估指数。首先，研究了所提出算法的鲁棒性，其次，评估了新的信号质量评估指数。
使用了来自三个不同研究的数据：睡眠研究、PhysioNet 2017 挑战赛和应激研究。来自具有心电图分析经验的不同注释者的清洁或污染的二进制标签可用。从 MIT-BIH 噪声应激测试数据库（NSTDB）添加了两种类型的现实心电图噪声到睡眠研究中，以测试质量指数。首先，在睡眠数据集上训练模型，然后在其他两个数据集的子集上进行测试。其次，通过在三个数据集的子集上训练模型来考虑所有记录条件。最后，比较模型对于注释者之间不同一致性水平的后验概率。
当在睡眠数据集上训练模型时，获得了 0.988 到 1.000 之间的 AUC 值。当在三个数据集上训练模型并考虑所有记录条件时，这些结果得到了进一步的提高。观察到干净类分数与一致性水平之间的 Pearson 相关系数为 0.8131。此外，对于两种添加的噪声类型，都观察到每噪声水平的信号质量显著降低。
本研究的主要新颖之处在于基于分类器的干净类后验概率的新的心电图信号质量评估方法。
