Bashar Syed Khairul, Ding Eric, Walkey Allan J, McManus David D, Chon Ki H
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA.
IEEE Access. 2019;7:88357-88368. doi: 10.1109/access.2019.2926199. Epub 2019 Jul 1.
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.
在重症监护病房（ICU）记录的长期心电图（ECG）信号常常受到严重的运动和噪声伪影（MNA）干扰，这可能导致许多误报，包括心房颤动（AF）检测不准确。我们开发了一种自动方法，用于从重症监护医学信息数据库（MIMIC）III中的心电图记录中检测MNA。由于AF检测通常基于对QRS复合波导出的不规则RR间期的识别，我们的MNA检测算法的主要设计重点是识别心电图信号中受损的QRS复合波。MIMIC III数据库中的MNA不仅包含运动引起的噪声，还包含大量非心电图波形，这些也必须自动识别。我们的算法旨在首先利用时域和频域特性区分心电图和非心电图波形。对于包含心电图波形的数据段，分别使用时频谱及其子带分解方法来识别MNA和高频噪声心电图段。该算法在35名正常窦性心律受试者和25名AF受试者的数据上进行了测试。结果表明，即使在某些受试者中后者数量众多，所提出的方法也能准确区分包含真实心电图波形的段和不包含真实心电图波形的段。此外，我们发现当使用我们的MNA检测算法时，正常受试者中AF的误报检测显著减少（>94%）。如果不使用它，由于MNA，我们会不准确地检测到AF。
