Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients

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 the corrupted QRS complexes of the ECG signals. The MNA in the MIMIC III database contains 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 the 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 subband 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 due to the MNA.