Contactless cardiac arrest detection using smart devices
Contactless cardiac arrest detection using smart devices
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DOI：
10.1038/s41746-019-0128-7
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发表时间：
2019-06-19
影响因子：
15.2
通讯作者：
Sunshine, Jacob E.
中科院分区：
文献类型：
--
作者：
Chan, Justin;Rea, Thomas;Sunshine, Jacob E.
院外心脏骤停是世界范围内的主要死亡原因。快速诊断和启动心肺复苏（CPR）是心脏骤停患者治疗的基石。然而，很大一部分心脏骤停的受害者没有生存的机会，因为他们经历了一个无人目睹的事件，往往在自己的家中隐私。心脏骤停的一个未被充分认识的诊断要素是濒死呼吸的存在，这是一种听觉生物标志物和在严重缺氧情况下出现的脑干反射。在这里，我们证明了支持向量机（SVM）可以在卧室环境中实时分类濒死呼吸实例。使用真实世界标记的心脏骤停的9-1-1音频，我们训练SVM来准确地分类濒死呼吸实例。我们获得的曲线下面积（AUC）为0.9993 +/- 0.0003，操作点的总体灵敏度和特异性分别为97.24%（95%CI：96.86-97.61%）和99.51%（95%CI：99.35-99.67%）。我们在82小时（117，985个音频片段）的多导睡眠实验室数据（包括打鼾，呼吸不足，中枢性和阻塞性睡眠呼吸暂停事件）中实现了0到0.14%的假阳性率。我们还在家庭睡眠环境中评估了我们的分类器：在35个不同的卧室环境中收集的164小时（236，666个音频片段）的睡眠数据中，假阳性率为0-0.22%。我们使用商品智能设备（Amazon Echo和Apple iPhone）对我们的概念验证非接触式系统进行原型设计，并证明其在识别与心脏骤停相关的濒死呼吸情况方面的有效性。
Out-of-hospital cardiac arrest is a leading cause of death worldwide. Rapid diagnosis and initiation of cardiopulmonary resuscitation (CPR) is the cornerstone of therapy for victims of cardiac arrest. Yet a significant fraction of cardiac arrest victims have no chance of survival because they experience an unwitnessed event, often in the privacy of their own homes. An under-appreciated diagnostic element of cardiac arrest is the presence of agonal breathing, an audible biomarker and brainstem reflex that arises in the setting of severe hypoxia. Here, we demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment. Using real-world labeled 9-1-1 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances. We obtain an area under the curve (AUC) of 0.9993 +/- 0.0003 and an operating point with an overall sensitivity and specificity of 97.24% (95% CI: 96.86-97.61%) and 99.51% (95% CI: 99.35-99.67%). We achieve a false positive rate between 0 and 0.14% over 82 h (117,985 audio segments) of polysomnographic sleep lab data that includes snoring, hypopnea, central, and obstructive sleep apnea events. We also evaluate our classifier in home sleep environments: the false positive rate was 0-0.22% over 164 h (236,666 audio segments) of sleep data collected across 35 different bedroom environments. We prototype our proof-of-concept contactless system using commodity smart devices (Amazon Echo and Apple iPhone) and demonstrate its effectiveness in identifying cardiac arrest-associated agonal breathing instances played over the air.