A practical algorithm to reduce false critical ECG alarms using arterial blood pressure and/or photoplethysmogram...

Area of Science:

• Biomedical Engineering
• Critical Care Medicine
• Signal Processing

Background:

• High rates of false critical electrocardiogram (ECG) arrhythmia alarms in intensive care units (ICUs) contribute to alarm fatigue and can jeopardize patient safety.
• The 'crying-wolf' syndrome, resulting from frequent false alarms, necessitates improved alarm systems for critical care environments.

Purpose of the Study:

• To develop and evaluate an algorithm that reduces false critical arrhythmia alarms by utilizing arterial blood pressure (ABP) and/or photoplethysmogram (PPG) waveform features.
• To improve patient safety and reduce alarm fatigue in ICUs by enhancing the accuracy of critical ECG arrhythmia detection.

Main Methods:

• Established long-duration reference alarm datasets comprising 573 ICU waveform-alarm records (551 patent days) with continuous ECG, ABP, and/or PPG signals.
• Developed an algorithm that extracts beat-by-beat ABP/PPG pulse features and generates five event feature indicators (EFIs) for critical ECG alarms.
• Adjudicated critical ECG alarms by checking corresponding EFI values of ABP/PPG pulses around the alarm time to accept or reject the alarm.

Main Results:

• The algorithm demonstrated the ability to retain 100% of true critical ECG alarms.
• A significant reduction in false critical arrhythmia alarms was achieved using the proposed algorithm.
• The algorithm proved effective and practical due to its real-time dynamic processing and computational efficiency.

Conclusions:

• The developed algorithm effectively reduces false critical arrhythmia alarms in ICUs by integrating ABP/PPG waveform features.
• This approach offers a practical and computationally efficient solution for improving the reliability of ECG monitoring in critical care settings.
• The algorithm's ability to maintain sensitivity for true alarms while reducing false positives holds significant promise for enhancing patient safety and clinical workflow in ICUs.