title: Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators.
abstract: Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survival of out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrillators (AED). AED algorithms for VF-detection are customarily assessed using Holter recordings from public electrocardiogram (ECG) databases, which may be different from the ECG seen during OHCA events. This study evaluates VF-detection using data from both OHCA patients and public Holter recordings. ECG-segments of 4-s and 8-s duration were analyzed. For each segment 30 features were computed and fed to state of the art machine learning (ML) algorithms. ML-algorithms with built-in feature selection capabilities were used to determine the optimal feature subsets for both databases. Patient-wise bootstrap techniques were used to evaluate algorithm performance in terms of sensitivity (Se), specificity (Sp) and balanced error rate (BER). Performance was significantly better for public data with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of 94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times more features than the data from public databases for an accurate detection (6 vs 3). No significant differences in performance were found for different segment lengths, the BER differences were below 0.5-points in all cases. Our results show that VF-detection is more challenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s.
authors.affiliation: Department of Telecommunication Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
authors.forename: Carlos
authors.identifier: http://orcid.org/0000-0002-2728-3552
authors.initials: C
authors.lastname: Figuera
authors.affiliation: Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
authors.forename: Unai
authors.initials: U
authors.lastname: Irusta
authors.affiliation: Department of Telecommunication Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Morgado
authors.affiliation: Department of Communication Engineering, University of the Basque Country UPV/EHU, Bilbao, Spain.
authors.forename: Elisabete
authors.initials: E
authors.lastname: Aramendi
authors.affiliation: Electronics and Computing Department, University of Mondragon, Mondragon, Spain.
authors.forename: Unai
authors.initials: U
authors.lastname: Ayala
authors.affiliation: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, Oslo, Norway.
authors.forename: Lars
authors.initials: L
authors.lastname: Wik
authors.affiliation: Norwegian National Advisory Unit on Prehospital Emergency Medicine (NAKOS), Oslo University Hospital and University of Oslo, Oslo, Norway.
authors.forename: Jo
authors.initials: J
authors.lastname: Kramer-Johansen
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway.
authors.forename: Trygve
authors.initials: T
authors.lastname: Eftestøl
authors.affiliation: Department of Telecommunication Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
authors.forename: Felipe
authors.initials: F
authors.lastname: Alonso-Atienza
mesh_terms: D000465:Algorithms; D001331:Automation; D019992:Databases as Topic; D047548:Defibrillators; D004562:Electrocardiography; D006801:Humans; D000069550:Machine Learning; D058687:Out-of-Hospital Cardiac Arrest; D013997:Time Factors