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首页> 外文期刊>Elektronika ir Elektrotechnika >Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier
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Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

机译：使用离散小波变换和决策树分类的ECG信号中基线 - 漫游噪声的检测和分类

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摘要

An electrocardiogram (ECG) signal is usually contaminated with various noises, such as baseline-wander, power-line interference, and electromyogram (EMG) noise. Denoising must be performed to extract meaningful information from ECG signals for clinical detection of heart diseases. This work is focused on baseline-wander noise as it shares the same frequency spectrum as the ST segment of ECG signals. Hence, it is important to estimate the baseline-wander prior to its removal from ECG signals. This paper presents a method for classifying each segment of the ECG signal's baseline-wander as minimal, moderate or large. We use the C4.5 decision tree algorithm to model the classifier using the WEKA data-mining tool. We test the proposed method on ECG signals obtained from the MIT-BIH arrhythmia database (48 ECG recordings, each slightly longer than 30 min). We use 36 ECG recordings for training the classifier with the remaining 12 ECG recordings as the test data for classification. We partition each recording into 5 second, non-overlapping segments, which result in 361 segments for each record. The classification results show that the model classifier achieves an average sensitivity of 97.36 %, specificity of 99.50 %, and overall accuracy of 98.89 % in classifying the baseline-wander noise in ECG signals. The proposed method effectively addresses the question of identifying the minimal baseline-wander segments. Moreover, the proposed framework may help in devising an algorithm for the selective filtering of moderate and large baseline-wander segments to achieve the best trade-off between accuracy and computational cost.
机译：心电图（ECG）信号通常污染各种噪声，例如基线 - 漂泊，电力线干扰和电灰度（EMG）噪声。必须执行去噪，以从ECG信号中提取有意义的信息，以临床检测心脏病。这项工作专注于基线 - 漫游噪声，因为它与ECG信号的ST段共享相同的频谱。因此，重要的是在从ECG信号中删除之前估计基线 - 徘徊。本文介绍了将ECG信号基线的每个段分类为最小，中等或大的方法。我们使用C4.5决策树算法使用Weka数据挖掘工具来模拟分类器。我们在从MIT-BIH心律失常数据库获得的ECG信号上测试所提出的方法（48个ECG录像，每个略微长于30分钟）。我们使用36个ECG录制来培训分类器，其中包含剩下的12个ECG记录作为分类的测试数据。我们将每个记录分为5秒，非重叠段，导致每条记录的361个段。分类结果表明，模型分类器实现了97.36％的平均灵敏度，特异性为99.50％，总体准确性为98.89％，在ECG信号中分类基线 - 漫游噪声。该方法有效地解决了识别最小基线 - 漫游段的问题。此外，所提出的框架可以帮助设计用于中等和大型基线 - 漫游段的选择性过滤的算法，以实现精度和计算成本之间的最佳权衡。

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