Decreasing False Alarms by Obtaining the Best Signal and Minimizing Artifact from Physiological Sensors

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.

ECG Monitoring during End of Life Care: Implications on Alarm Fatigue

Excessive numbers of clinical alarms in the intensive care unit (ICU) contribute to alarm fatigue. Efforts to eliminate unnecessary alarms, including during end of life (EOL) care, are pivotal. This study describes electrocardiographic (ECG) arrhythmia alarm usage following the decision for comfort care. We conducted a review of electronic health records (EHR) in patients who died and had comfort care orders that were in place during our study. The occurrences of ECG arrhythmia alarms among these patients were examined. We found 151 arrhythmia alarms that were generated in 11 patients after comfort care was initiated: 72% were audible, 21% were manually muted, and 7% had an unknown audio label. Level of alarm: 33% crisis, 58% warning, 1% message, and 8% were labeled unknown. Our report shows that ECG monitoring was commonly maintained during the EOL care. Since the goal of care during this phase is for both patient and family comfort, it is important for the clinicians to weigh the benefits versus harms of the continuous ECG monitoring.