Abstract
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.
Highlights
An intensive care unit (ICU) uses Food and Drug Administration-approved devices for monitoring various physiological signals of patients in critical condition [1]
The task of the motion artifacts (MNA) detection algorithm is to discriminate among three possible classifications: non-ECG waveforms, ECG with artifact contamination, and clean ECG
We illustrated the performance of our MNA detection algorithm, which accounted for various types of contamination including high frequency noise sources, using the long-term continuous ECG recordings obtained from the Medical Information Mart for Intensive Care (MIMIC) III database
Summary
An intensive care unit (ICU) uses Food and Drug Administration-approved devices for monitoring various physiological signals of patients in critical condition [1]. Among the physiological signals recorded in the ICU, electrocardiogram (ECG) is one of the most important vital sign alerts, but it is widely used for the diagnosis of cardiovascular diseases and associated arrhythmias [2]. In an ICU, the ECG is often severely corrupted by noise and motion artifacts (MNA) and may drop data due to poor electrode contact with the skin. Some common sources of ECG noise include, among other things, a bad electrode contact, motion artifacts, electromyography (EMG) noise, and baseline wander [5], [6]
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