Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality
Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality
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DOI：
10.1088/0967-3334/33/9/1435
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发表时间：
2012-09-01
影响因子：
3.2
通讯作者：
Langley, Philip
中科院分区：
文献类型：
--
作者：
Di Marco, Luigi Yuri;Duan, Wenfeng;Langley, Philip
一种新的算法，用于分类心电图记录质量的基础上检测常见的心电图污染物，往往使心电图无法用于诊断目的进行了评价。污染物（基线漂移，平线，QRS伪影，虚假尖峰，幅度逐步变化，噪声）检测到联合时间-频率分析和QRS振幅的个别导联。分类是基于级联单条件决策规则（SCDR），测试污染物的水平对分类阈值。一个监督学习分类器（SLC）进行比较。SCDR和SLC算法在“可接受”与“不可接受”质量记录的注释数据库（A组，PhysioNet Challenge 2011）上使用“leaveMout”方法进行训练，重复随机分区和交叉验证。考虑了两种训练方法：（i）平衡的，其中训练记录具有相等数量的“可接受的”和“不可接受的”记录，（ii）不平衡的，其中保留来自集合A的“可接受的”与“不可接受的”记录的比率。对于每种训练方法，计算阈值，并将算法的分类准确度与其他基于规则的算法和使用分类未知的数据库的SLC进行比较（集B PhysioNet挑战2011）。SCDR算法实现了最高的准确度（91.40%）相比，SLC（90.40%），尽管其简单的逻辑。它还提供了便于向用户报告不良信号质量的有意义的原因的优点。
A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of 'acceptable' versus 'unacceptable' quality recordings using the 'leaveMout' approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of 'acceptable' and 'unacceptable' recordings, (ii) unbalanced, in which the ratio of 'acceptable' to 'unacceptable' recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.