Objective

Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.

Methods

A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class.

Results

The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators.

Conclusions

The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

中文翻译：

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预测院外心脏骤停患者的除颤成功率：超越特征设计

目标

通过评估成功结果的可能性来优化除颤时间可以显着增强复苏。以前的研究采用传统的机器学习方法和手工制作的特征来解决这个问题，但没有一项研究取得了被广泛接受的卓越性能。本研究提出了一种自动学习预测特征的新方法。

方法

紧接在第一次除颤电击之前的原始 4s VF 事件被馈送到 3 级 CNN 特征提取器。每个阶段由 4 个组件组成：卷积、修正线性单元激活、dropout 和最大池化。在特征提取器的最后，特征图被展平并连接到一个全连接的多层感知器进行分类。对于模型评估，采用了 10 倍交叉验证。为了平衡类，SMOTE 过采样方法已应用于少数类。

结果

获得的结果表明，所提出的模型在预测除颤结果方面是高度准确的（Acc = 93.6 %）。由于对分类器的建议表明至少 50% 的特异性和 95% 的灵敏度作为除颤决策的安全和有用的预测因子，因此报告的灵敏度为 98.8% 和特异性为 88.2%，分析速度为 3 ms/输入信号，表明建议的模型具有在自动体外除颤器中实施的良好前景。

结论

当在同一数据集上执行时，学习到的特征表现出优于手工制作的特征。这种方法通过将特征提取、选择和分类融合到单个学习模型中而受益于全自动。它提供了一种优越的策略，可用作指导 OHCA 患者治疗的工具，从而实现优先治疗的最佳决策。此外，为了鼓励可复制性，该数据集已向研究界公开。