Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External DefibrillatorsReport as inadecuate

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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.

Author: Carlos Figuera , Unai Irusta, Eduardo Morgado, Elisabete Aramendi, Unai Ayala, Lars Wik, Jo Kramer-Johansen, Trygve Eftestøl, Fe



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