ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction
ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction
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DOI：
10.1109/tbme.2013.2240452
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发表时间：
2013-06-01
影响因子：
4.6
通讯作者：
Clifford, Gari D.
中科院分区：
文献类型：
--
作者：
Behar, Joachim;Oster, Julien;Clifford, Gari D.
为了抑制重症监护病房(ICU)监护仪的假心律失常报警，提出了一种自动评估正常和异常节律的心电质量的算法。对各种心律失常的质量评估给予了特别关注。使用了来自三个数据库的数据：Physionet Challenge2011数据集、MIT-BIH心律失常数据库和MIMIC II数据库。人工评估了33000多个单导联10个S心电节段的质量，并使用Physionet噪声负荷测试数据库生成了12000个质量不佳的单导联心电节段。信号质量指数(SQI)是从ECG片段中提取的，并被用作具有高斯核的支持向量机分类器的输入。该分类器被训练来估计心电分段的质量。在训练和测试集上，正常窦性心律的分类准确率高达99%，心律失常的分类准确率高达95%，尽管不同类型的节律表现差异很大。此外，还研究了来自MIMIC II数据库的4050个ICU警报与分类器评估的信号质量之间的关联。结果表明，SQI应该是节奏特定的，分类器应该针对每个节奏呼叫进行独立的训练。这将需要大幅增加的标签数据集，以便训练准确的算法。
An automated algorithm to assess electrocardiogram (ECG) quality for both normal and abnormal rhythms is presented for false arrhythmia alarm suppression of intensive care unit (ICU) monitors. A particular focus is given to the quality assessment of a wide variety of arrhythmias. Data from three databases were used: the Physionet Challenge 2011 dataset, the MIT-BIH arrhythmia database, and the MIMIC II database. The quality of more than 33 000 single-lead 10 s ECG segments were manually assessed and another 12 000 bad-quality single-lead ECG segments were generated using the Physionet noise stress test database. Signal quality indices (SQIs) were derived from the ECGs segments and used as the inputs to a support vector machine classifier with a Gaussian kernel. This classifier was trained to estimate the quality of an ECG segment. Classification accuracies of up to 99% on the training and test set were obtained for normal sinus rhythm and up to 95% for arrhythmias, although performance varied greatly depending on the type of rhythm. Additionally, the association between 4050 ICU alarms from the MIMIC II database and the signal quality, as evaluated by the classifier, was studied. Results suggest that the SQIs should be rhythm specific and that the classifier should be trained for each rhythm call independently. This would require a substantially increased set of labeled data in order to train an accurate algorithm.