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首页> 外文会议>International Symposium on Medical Measurements and Applications >Detection of Noise Type in Electrocardiogram
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Detection of Noise Type in Electrocardiogram

机译：心电图中噪声类型的检测

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Physicians use electrocardiogram (ECG) to diagnose cardiovascular diseases. It is mainly used in hospital environment; however, with advancements in ambulatory ECG, it is now available outside of hospital environment. Ambulation can lead to contamination of ECG with various noises leading to signal corruption, misdiagnosis, or false alarms. Removal of noise from ECG is possible; however, blindly applying noise removal techniques may reduce the fidelity of the ECG. As such, identification of the noise in the ECG and applying targeted techniques minimize information loss. In this study, a machine learning approach is used to identify the type of noise in ECG. ECG from Physionet's Normal S inus Rhythm Database was contaminated with noise (baseline wandering, electrode motion, and electromyography) from Physionet's MIT-BIH Noise S tress Test Database at different levels and combinations. The chosen machine learning algorithm was Random Forest with 1024 estimators. The Random Forest had a precision and recall of 1.0 when identifying clean ECG. The average precision and recall were 0.47 and 0.63, respectively, for segments with a single type of noise. The average precision and recall were were 0.44 and 0.27, respectively, for segments with multiple types of noise. The drop in precision and recall was due to the misclassification of the ECG with multiple noises as ECG with a single noise; as an example, classification of an ECG with baseline wandering and electromyography as an ECG with baseline wandering. The classifier performed well at identifying any of the noises in segments with multiple types of noises with an average precision and recall of 0.81 and 0.70, respectively. The classifier generally performed well in identifying types of noise in ECG allowing for future work in developing a framework for identification and mitigation of noise.
机译：医师使用心电图（ECG）诊断心血管疾病。主要用于医院环境;但是，随着动态心电图的发展，现在可以在医院环境之外使用它。移动可能会导致ECG受到各种噪声的污染，从而导致信号损坏，误诊或误报。可以消除ECG的噪音;但是，盲目应用降噪技术可能会降低ECG的保真度。这样，在ECG中识别噪声并应用有针对性的技术可最大程度地减少信息丢失。在这项研究中，使用机器学习方法来识别ECG中的噪声类型。 Physionet正常窦性心律数据库中的ECG被Physionet MIT-BIH噪声测试数据库中的噪声（基线漂移，电极运动和肌电图）以不同的水平和组合污染。选择的机器学习算法是具有1024个估计量的随机森林。识别干净的ECG时，Random Forest的精度和召回率为1.0。对于具有单一噪声类型的段，平均精度和召回率分别为0.47和0.63。对于具有多种类型噪声的线段，平均精度和召回率分别为0.44和0.27。精度和召回率的下降是由于将具有多种噪声的ECG误分类为具有单一噪声的ECG所致;例如，将具有基线漂移的ECG进行分类，并将肌电图分类为具有基线漂移的ECG。分类器在识别具有多种类型噪声的段中的任何噪声时表现良好，平均精度和查全率分别为0.81和0.70。该分类器在识别ECG中的噪声类型方面通常表现良好，从而允许将来在开发用于识别和缓解噪声的框架中开展工作。

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