Key result
The real-time arrhythmia detection library (ADLib) achieved an optimized real-time score of 80.07 in the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU data.
Why the study?
Does a real-time arrhythmia detection library with supplementary ECG quality and pulse wave monitoring improve the accuracy of arrhythmia alarms in ICU data?
Does a real-time arrhythmia detection library with supplementary ECG quality and pulse wave monitoring improve the accuracy of arrhythmia alarms in ICU data?
The application of a real-time arrhythmia detection library with supplementary ECG and pulse wave monitoring provides robust detection of life-threatening arrhythmias, potentially reducing false ICU alarms.
May reduce false ICU alarms; leaves open prospective outcome trials before practice change.
False intensive care unit (ICU) alarms induce stress in both patients and clinical staff and decrease the quality of care, thus significantly increasing both the hospital recovery time and rehospitalization rates. In the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU bedside monitor data, this paper validates the application of a real-time arrhythmia detection library (ADLib, Schiller AG) for the robust detection of five types of life-threatening arrhythmia alarms. The strength of the application is to give immediate feedback on the arrhythmia event within a scan interval of 3 s-7.5 s, and to increase the noise immunity of electrocardiogram (ECG) arrhythmia analysis by fusing its decision with supplementary ECG quality interpretation and real-time pulse wave monitoring (quality and hemodynamics) using arterial blood pressure or photoplethysmographic signals. We achieved the third-ranked real-time score (79.41) in the challenge (Event 1), however, the rank was not officially recognized due to the 'closed-source' entry. This study shows the optimization of the alarm decision module, using tunable parameters such as the scan interval, lead quality threshold, and pulse wave features, with a follow-up improvement of the real-time score (80.07). The performance (true positive rate, true negative rate) is reported in the blinded challenge test set for different arrhythmias: asystole (83%, 96%), extreme bradycardia (100%, 90%), extreme tachycardia (98%, 80%), ventricular tachycardia (84%, 82%), and ventricular fibrillation (78%, 84%). Another part of this study considers the validation of ADLib with four reference ECG databases (AHA, EDB, SVDB, MIT-BIH) according to the international recommendations for performance reports in ECG monitors (ANSI/AAMI EC57). The sensitivity (Se) and positive predictivity (+P) are: QRS detector QRS (Se, +P) > 99.7%, ventricular ectopic beat (VEB) classifier VEB (Se, +P) = 95%, and ventricular fibrillation detector VFIB (P + = 94.8%) > VFIB (Se = 86.4%), adjusted to the clinical setting requirements, giving preference to low false positive alarms.
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Krasteva et al. (2016) studied Life-threatening arrhythmia alarms in ICU bedside monitor data. Real-time arrhythmia detection library (ADLib) with supplementary ECG quality and pulse wave monitoring was evaluated on Real-time score in the PhysioNet/CinC Challenge 2015 (Event 1). The real-time arrhythmia detection library (ADLib) achieved an optimized real-time score of 80.07 in the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU data.
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