Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway; NeuroGroup, BioMediTech and Faculty of Medicine and Life Sciences, University of Tampere, 33520 Tampere, Finland.
Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
Resuscitation. 2018 Jan;122:6-12. doi: 10.1016/j.resuscitation.2017.11.035. Epub 2017 Nov 6.
An automatic resuscitation rhythm annotator (ARA) would facilitate and enhance retrospective analysis of resuscitation data, contributing to a better understanding of the interplay between therapy and patient response. The objective of this study was to define, implement, and demonstrate an ARA architecture for complete resuscitation episodes, including chest compression pauses (CC-pauses) and chest compression intervals (CC-intervals).
We analyzed 126.5h of ECG and accelerometer-based chest-compression depth data from 281 out-of-hospital cardiac arrest (OHCA) patients. Data were annotated by expert reviewers into asystole (AS), pulseless electrical activity (PEA), pulse-generating rhythm (PR), ventricular fibrillation (VF), and ventricular tachycardia (VT). Clinical pulse annotations were based on patient-charts and impedance measurements. An ARA was developed for CC-pauses, and was used in combination with a chest compression artefact removal filter during CC-intervals. The performance of the ARA was assessed in terms of the unweighted mean of sensitivities (UMS).
The UMS of the ARA were 75.0% during CC-pauses and 52.5% during CC-intervals, 55-points and 32.5-points over a random guess (20% for five categories). Filtering increased the UMS during CC-intervals by 5.2-points. Sensitivities for AS, PEA, PR, VF, and VT were 66.8%, 55.8%, 86.5%, 82.1% and 83.8% during CC-pauses; and 51.1%, 34.1%, 58.7%, 86.4%, and 32.1% during CC-intervals.
A general ARA architecture was defined and demonstrated on a comprehensive OHCA dataset. Results showed that semi-automatic resuscitation rhythm annotation, which may involve further revision/correction by clinicians for quality assurance, is feasible. The performance (UMS) dropped significantly during CC-intervals and sensitivity was lowest for PEA.
自动复苏节律注释器（ARA）将有助于并增强复苏数据的回顾性分析，有助于更好地理解治疗与患者反应之间的相互作用。本研究的目的是定义、实施和演示用于完整复苏事件的 ARA 架构，包括胸外按压暂停（CC 暂停）和胸外按压间隔（CC 间隔）。
我们分析了 281 例院外心脏骤停（OHCA）患者的 126.5 小时心电图和基于加速度计的胸外按压深度数据。数据由专家评审员注释为停搏（AS）、无脉电活动（PEA）、产生脉搏节律（PR）、心室颤动（VF）和室性心动过速（VT）。临床脉搏注释基于患者图表和阻抗测量。开发了用于 CC 暂停的 ARA，并在 CC 间隔期间与胸部按压伪影去除滤波器结合使用。ARA 的性能通过未加权平均灵敏度（UMS）进行评估。
ARA 在 CC 暂停期间的 UMS 为 75.0%，在 CC 间隔期间为 52.5%，比随机猜测高 55 个点和 32.5 个点（五个类别为 20%）。过滤在 CC 间隔期间提高了 5.2 个点的 UMS。在 CC 暂停期间，AS、PEA、PR、VF 和 VT 的灵敏度分别为 66.8%、55.8%、86.5%、82.1%和 83.8%；在 CC 间隔期间，灵敏度分别为 51.1%、34.1%、58.7%、86.4%和 32.1%。
定义并展示了一种通用的 ARA 架构，该架构在全面的 OHCA 数据集上进行了演示。结果表明，半自动复苏节律注释（可能需要临床医生进一步修订/更正以确保质量）是可行的。在 CC 间隔期间，性能（UMS）显著下降，PEA 的灵敏度最低。
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