Shock advisory system with minimal delay triggering after end of chest compressions: Accuracy and gained hands-off time

Didon, Jean-Philippe; Krasteva, Vessela; Ménétré, Sarah; Stoyanov, Todor; Jekova, Irena

doi:10.1016/s0300-9572(11)70145-9

Cited by 27 publications

(23 citation statements)

References 19 publications

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“…Currently, AED analyses require segments longer than 6 s [ 52 ]. Our results confirm that it could be safely shortened to 4 s, in line with some recently published data [ 38 , 39 , 53 ].…”

Section: Discussionsupporting

confidence: 93%

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

et al. 2016

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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.

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“…Currently, AED analyses require segments longer than 6 s [ 52 ]. Our results confirm that it could be safely shortened to 4 s, in line with some recently published data [ 38 , 39 , 53 ].…”

Section: Discussionsupporting

confidence: 93%

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

et al. 2016

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“…The RLS filter we propose is a more efficient estimator of the ICC component than our previous LMS method, 22 and permits a reliable PEA/PR discrimination using shorter ECG/TI segments of duration below 5 s with minimal computational burden, around 50ms (see supplementary materials). This would limit the duration of analysis segments to values comparable to those used for rhythm analysis in defibrillators 27,28 as long as the segment stored in the defibrillator is immediately analyzed by the circulation detector after an OR is detected by the SAA. This would shorten pauses in chest compressions and may therefore contribute to improve survival since longer pauses compromise coronary and cerebral blood flow.…”

Section: Discussionmentioning

confidence: 99%

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

et al. 2016

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Aim To develop and evaluate a method to detect circulation in the presence of organized rhythms (ORs) during resuscitation using signals acquired by defibrillation pads. Methods Segments containing electrocardiogram (ECG) and thoracic impedance (TI) signals free of artifacts were used. The ECG corresponded to ORs classified as pulseless electrical acitivity (PEA) or pulse-generating rhythm (PR). A first dataset containing 1091 segments was split into training and test sets to develop and validate the circulation detector. The method processed ECG and TI to obtain the impedance circulation component (ICC). Morphological features were extracted from ECG and ICC, and combined into a classifier to discriminate between PEA and PR. The performance of the method was evaluated in terms of sensitivity (PR) and specificity (PEA). A second dataset (86 segments from different patients) was used to assess two application of the method: confirmation of arrest by recognizing absence of circulation during ORs and detection of return of spontaneous circulation (ROSC) during resuscitation. In both cases, time to confirmation of arrest/ROSC was determined. Results The method showed a sensitivity/specificity of 92.1%/90.3% and 92.2%/91.9% for training and test sets respectively. The method confirmed cardiac arrest with a specificity of 93.3% with a median delay of 0 s after the first OR annotation. ROSC was detected with a sensitivity of 94.4% with a median delay of 57 s from ROSC onset. Conclusion The method showed good performance, and can be reliably used to distinguish perfusing from non-perfusing ORs.

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“…Strategies to shorten pre-shock pauses involve compressions during defibrillator charging, 9,10 simplifying voice prompts, 11 or immediately triggering rhythm analysis at the end of chest compressions. 12 However, interruptions for rhythm analysis are necessary because chest compression artefacts may confound the diagnosis of the shock advice algorithm (SAA) in current AEDs. 13 These interruptions contribute to the pre-shock pause, which lasts between 5.2 and 28.4 s 14 and may compromise the survival of the patient.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic rhythm analysis during chest compression pauses

et al. 2015

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Shock advisory system with minimal delay triggering after end of chest compressions: Accuracy and gained hands-off time

Cited by 27 publications

References 19 publications

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Circulation detection using the electrocardiogram and the thoracic impedance acquired by defibrillation pads

Fully automatic rhythm analysis during chest compression pauses

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