Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients.
Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients.
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DOI：
10.1109/access.2019.2926199
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Chon KH
中科院分区：
文献类型：
--
作者：
Bashar SK;Ding E;Walkey AJ;McManus DD;Chon KH
在重症监护病房（ICU）记录的长期心电图（ECG）信号经常被严重的运动和噪声伪影（MNA）所破坏，这可能导致许多误报，包括房颤（AF）的不准确检测。我们开发了一种从重症监护医学信息市场（MIMIC） III数据库中的心电图记录中检测MNA的自动化方法。由于AF检测通常基于识别由QRS复合体衍生的不规则RR区间，因此我们的MNA检测算法的主要设计重点是识别心电信号的损坏QRS复合体。MIMIC III数据库中的MNA不仅包含运动引起的噪声，还包含大量必须自动识别的非ecg波形。我们的算法首先利用时间和频谱域特性来区分心电和非心电波形。对于包含心电波形的数据片段，分别采用时频频谱及其子带分解方法对MNA和高频噪声心电片段进行识别。该算法在35例正常窦性心律患者和25例AF患者的数据上进行了测试。所提出的方法被证明可以准确地区分包含真实心电图波形的片段和不包含真实心电图波形的片段，即使后者在某些受试者中数量众多。此外，我们发现，当我们的MNA检测算法被使用时，正常受试者AF的假阳性检测显著减少（b> 94%）。在不使用它的情况下，由于MNA的存在，我们无法准确地检测到AF。
Long term electrocardiogram (ECG) signals recorded in an intensive care unit (ICU) are often corrupted by severe motion and noise artifacts (MNA), which may lead to many false alarms including inaccurate detection of atrial fibrillation (AF). We developed an automated method to detect MNA from ECG recordings in the Medical Information Mart for Intensive Care (MIMIC) III database. Since AF detection is often based on identification of irregular RR intervals derived from the QRS complexes, the main design focus of our MNA detection algorithm was to identify corrupted QRS complexes of the ECG signals. The MNA in the MIMIC III database contain not only motion-induced noise, but also a plethora of non-ECG waveforms, which must also be automatically identified. Our algorithm is designed to first discriminate between ECG and non-ECG waveforms using both time and spectral-domain properties. For the segments of data containing ECG waveforms, a time-frequency spectrum and its sub-band decomposition approach were used to identify MNA, and high frequency noise ECG segments, respectively. The algorithm was tested on data from 35 subjects in normal sinus rhythm and 25 AF subjects. The proposed method is shown to accurately discriminate between segments that contained real ECG waveforms and those that did not, even though the latter were numerous in some subjects. In addition, we found a significant reduction (> 94%) in false positive detection of AF in normal subjects when our MNA detection algorithm was used. Without using it, we inaccurately detected AF owing to the MNA.
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影响因子：
3.8
作者：
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发表时间：
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