title: Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for the reduction of false alarms in ICUs.
abstract: 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.
authors.affiliation: Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad G Bonchev Str. Bl 105, 1113 Sofia, Bulgaria.
authors.forename: Vessela
authors.initials: V
authors.lastname: Krasteva
authors.forename: Irena
authors.initials: I
authors.lastname: Jekova
authors.forename: Remo
authors.initials: R
authors.lastname: Leber
authors.forename: Ramun
authors.initials: R
authors.lastname: Schmid
authors.forename: Roger
authors.initials: R
authors.lastname: Abächerli
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D056902:Clinical Alarms; D004562:Electrocardiography / Q000295:instrumentation*; D005189:False Positive Reactions; D006801:Humans; D007362:Intensive Care Units; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D063177:Pulse Wave Analysis / Q000295:instrumentation*; D011786:Quality Control; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors