Showing papers by "Jo Kramer-Johansen published in 2019"

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Abstract: Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

A Machine Learning Shock Decision Algorithm for Use During Piston-Driven Chest Compressions

Abstract: Goal: Accurate shock decision methods during piston-driven cardiopulmonary resuscitation (CPR) would contribute to improve therapy and increase cardiac arrest survival rates. The best current methods are computationally demanding, and their accuracy could be improved. The objective of this work was to introduce a computationally efficient algorithm for shock decision during piston-driven CPR with increased accuracy. Methods: The study dataset contains 201 shockable and 844 nonshockable ECG segments from 230 cardiac arrest patients treated with the LUCAS-2 mechanical CPR device. Compression artifacts were removed using the state-of-the-art adaptive filters, and shock/no-shock discrimination features were extracted from the stationary wavelet transform analysis of the filtered ECG, and fed to a support vector machine (SVM) classifier. Quasi-stratified patient wise nested cross-validation was used for feature selection and SVM hyperparameter optimization. The procedure was repeated 50 times to statistically characterize the results. Results: Best results were obtained for a six-feature classifier with mean (standard deviation) sensitivity, specificity, and total accuracy of 97.5 (0.4), 98.2 (0.4), and 98.1 (0.3), respectively. The algorithm presented a five-fold reduction in computational demands when compared to the best available methods, while improving their balanced accuracy by 3 points. Conclusions: The accuracy of the best available methods was improved while drastically reducing the computational demands. Significance: An efficient and accurate method for shock decisions during mechanical CPR is now available to improve therapy and contribute to increase cardiac arrest survival.

Automatic Cardiac Rhythm Classification With Concurrent Manual Chest Compressions

Abstract: Electrocardiogram (EKG) based classification of out-of-hospital cardiac arrest (OHCA) rhythms is important to guide treatment and to retrospectively elucidate the effects of therapy on patient response. OHCA rhythms are grouped into five categories: ventricular fibrillation (VF) and tachycardia (VT), asystole (AS), pulseless electrical activity (PEA), and pulse-generating rhythms (PR). Clinically these rhythms are grouped into broader categories like shockable (VF/VT), non-shockable (AS/PEA/PR), or organized (ORG, PEA/PR). OHCA rhythm classification is further complicated because EKGs are corrupted by cardiopulmonary resuscitation (CPR) artifacts. The objective of this study was to demonstrate a framework for automatic multiclass OHCA rhythm classification in the presence of CPR artifacts. In total, 2133 EKG segments from 272 OHCA patients were used: 580 AS, 94 PR, 953 PEA, 479 VF, and 27 VT. CPR artifacts were adaptively filtered, 93 features were computed from the stationary wavelet transform analysis, and random forests were used for classification. A repeated stratified nested cross-validation procedure was used for feature selection, parameter tuning, and model assessment. Data were partitioned patient-wise. The classifiers were evaluated using per class sensitivity, and the unweighted mean of sensitivities (UMS) as a global performance metric. Four levels of clinical detail were studied: shock/no-shock, shock/AS/ORG, VF/VT/AS/ORG, and VF/VT/AS/PEA/PR. The median UMS (interdecile range) for the 2, 3, 4, and 5-class classifiers were: 95.4% (95.1-95.6), 87.6% (87.3-88.1), 80.6% (79.3-81.8), and 71.9% (69.5-74.6), respectively. For shock/no-shock decisions sensitivities were 93.5% (93.0-93.9) and 97.2% (97.0-97.4), meeting clinical standards for artifact-free EKG. The UMS for five classes with CPR artifacts was 5.8-points below that of the best algorithms without CPR artifacts, but improved the UMS of latter by over 19-points for EKG with CPR artifacts. A robust and accurate approach for multiclass OHCA rhythm classification during CPR has been demonstrated, improving the accuracy of the current state-of-the-art methods.

A Multistage Algorithm for ECG Rhythm Analysis During Piston-Driven Mechanical Chest Compressions

Abstract: Goal: An accurate rhythm analysis during cardiopulmonary resuscitation (CPR) would contribute to increase the survival from out-of-hospital cardiac arrest. Piston-driven mechanical compression devices are frequently used to deliver CPR. The objective of this paper was to design a method to accurately diagnose the rhythm during compressions delivered by a piston-driven device. Methods: Data was gathered from 230 out-of-hospital cardiac arrest patients treated with the LUCAS 2 mechanical CPR device. The dataset comprised 201 shockable and 844 nonshockable ECG segments, whereof 270 were asystole (AS) and 574 organized rhythm (OR). A multistage algorithm (MSA) was designed, which included two artifact filters based on a recursive least squares algorithm, a rhythm analysis algorithm from a commercial defibrillator, and an ECG-slope-based rhythm classifier. Data was partitioned randomly and patient-wise into training (60%) and test (40%) for optimization and validation, and statistically meaningful results were obtained repeating the process 500 times. Results: The mean (standard deviation) sensitivity (SE) for shockable rhythms, specificity (SP) for nonshockable rhythms, and the total accuracy of the MSA solution were: 91.7 (6.0), 98.1 (1.1), and 96.9 (0.9), respectively. The SP for AS and OR were 98.0 (1.7) and 98.1 (1.4), respectively. Conclusions: The SE/SP were above the 90%/95% values recommended by the American Heart Association for shockable and nonshockable rhythms other than sinus rhythm, respectively. Significance: It is possible to accurately diagnose the rhythm during mechanical chest compressions and the results considerably improve those obtained by previous algorithms.

Transthoracic Impedance Measured with Defibrillator Pads—New Interpretations of Signal Change Induced by Ventilations

Abstract: Compressions during the insufflation phase of ventilations may cause severe pulmonary injury during cardiopulmonary resuscitation (CPR). Transthoracic impedance (TTI) could be used to evaluate how chest compressions are aligned with ventilations if the insufflation phase could be identified in the TTI waveform without chest compression artifacts. Therefore, the aim of this study was to determine whether and how the insufflation phase could be precisely identified during TTI. We synchronously measured TTI and airway pressure (Paw) in 21 consenting anaesthetised patients, TTI through the defibrillator pads and Paw by connecting the monitor-defibrillator’s pressure-line to the endotracheal tube filter. Volume control mode with seventeen different settings were used (5–10 ventilations/setting): Six volumes (150–800 mL) with 12 min−1 frequency, four frequencies (10, 12, 22 and 30 min−1) with 400 mL volume, and seven inspiratory times (0.5–3.5 s ) with 400 mL/10 min−1 volume/frequency. Median time differences (quartile range) between timing of expiration onset in the Paw-line (PawEO) and the TTI peak and TTI maximum downslope were measured. TTI peak and PawEO time difference was 579 (432–723) m s for 12 min−1, independent of volume, with a negative relation to frequency, and it increased linearly with inspiratory time (slope 0.47, R 2 = 0.72). PawEO and TTI maximum downslope time difference was between −69 and 84 m s for any ventilation setting (time aligned). It was independent ( R 2 < 0.01) of volume, frequency and inspiratory time, with global median values of −47 (−153–65) m s , −40 (−168–68) m s and 20 (−93–128) m s , for varying volume, frequency and inspiratory time, respectively. The TTI peak is not aligned with the start of exhalation, but the TTI maximum downslope is. This knowledge could help with identifying the ideal ventilation pattern during CPR.

Percutaneous vs surgical emergency cricothyroidotomy: An experimental randomized crossover study on an animal-larynx model.

Abstract: BACKGROUND Airway management is a paramount clinical skill for the anaesthesiologist. The Emergency Cricothyroidotomy (EC) constitutes the final step in difficult airway algorithms securing a patent airway via a front-of-neck access. The main distinction among available techniques is whether the procedure is surgical and scalpel-based or percutaneous and needle-based. METHODS In an experimental randomized crossover trial, using an animal larynx model, we compared two EC techniques; the Rapid Four Step Technique and the Melker Emergency Cricothyrotomy Kit®. We assessed time expenditure and success rates among 20 anaesthesiologists and related this to previous training, seniority and clinical experience with EC. RESULTS All participants achieved successful airway access with both methods. Average time to successful airway access for scalpel-based EC was 54 (±31) seconds and for percutaneous EC 89 (±38) seconds, with 35 (95% CI: 14-57) seconds time difference, P = .003. Doctors with recent (<12 months) EC training performed better compared to the non-training group (37 vs 61 seconds, P = .03 for scalpel-based EC, and 65 vs 99 seconds, P = .02 for percutaneous EC). We found no differences according to clinical seniority or previous real-life EC experience. CONCLUSIONS Our study demonstrated that anaesthesiologists achieved successful airway access on an animal experimental model with both EC methods within a reasonable time frame, but the scalpel-based EC is performed more promptly. Recent EC training affected the time expenditure positively, while seniority and clinical EC experience did not. EC procedures should be regularly trained for.

A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR

Abstract: Chest compressions delivered during cardiopulmonary resuscitation (CPR) induce artifacts in the ECG that may make the shock advice algorithms (SAA) of defibrillators inaccurate. There is evidence that methods consisting of adaptive filters that remove the CPR artifact followed by machine learning (ML) based algorithms are able to make reliable shock/no-shock decisions during compressions. However, there is room for improvement in the performance of these methods. The objective was to design a robust ML framework for a reliable shock/no-shock decision during CPR. The study dataset contained 596 shockable and 1697 nonshockable ECG segments obtained from 273 cases of out-of-hospital cardiac arrest. Shock/no-shock labels were adjudicated by expert reviewers using ECG intervals without artifacts. First, CPR artifacts were removed from the ECG using a Least Mean Squares (LMS) filter. Then, 38 shock/no-shock decision features based on the Stationary Wavelet Transform (SWT) were extracted from the filtered ECG. A wapper-based feature selection method was applied to select the 6 best features for classification. Finally, 4 state-of-the-art ML classifiers were tested to make the shock/no-shock decision. These diagnoses were compared with the rhythm annotations to compute the Sensitivity (Se) and Specificity (Sp). All classifiers achieved an Se above 94.5%, Sp above 95.5% and an accuracy around 96.0%. They all exceeded the 90% Se and 95% Sp minimum values recommended by the American Heart Association.

Abstract 267: Injuries Associated With Mechanical Chest Compressions in Patients With Out-Of-Hospital Cardiac Arrest: A Comparison of LUCAS 2 and LUCAS 2 Active Decompression

Abstract: Background: Skeletal injuries (rib or sternum fractures) are common complications after cardiopulmonary resuscitation (CPR). Visceral injuries are also reported. During manual chest compressions, i...

Sammen redder vi liv

Abstract: Training in first aid has a long tradition in Norway. We believe that survival after time-critical events outside hospital can be further improved through systematic training.