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Predictive Diagnosis of Fatal Heart Rhythms Using Wearables

机译:可穿戴设备对致命性心律的预测诊断

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Sudden cardiac death causes more than 300,000 deaths annually in the US. Our research goal is to develop a continuous cardiac monitoring system that utilizes current wearable devices and is capable of not just detecting arrhythmia but also to predict life-threatening arrhythmia a few minutes before it would actually happen. The monitoring system should provide a diagnosis based on analyzing a few-minutes of heart-rate data streams. In order to verify the feasibility of this approach, we have developed a prototype and evaluated its capabilities. The prototype is based on a two-tier data analytics approach and utilizes multiple gradient boosting machine learning models. The system was tested for predicting four different life-threatening arrhythmias solely on realistic heart-rate readings and also tested the atrial fibrillation recognition capability. The prototype scored 91.6% and 93.9% accuracy respectively. These preliminary results validate the feasibility of our approach to predict arrhythmia in real-time from heart-rate observations.
机译:在美国,突发性心源性死亡每年导致30万多人死亡。我们的研究目标是开发一种连续的心脏监测系统,该系统利用当前的可穿戴设备,不仅能够检测出心律不齐,而且还可以在实际发生前几分钟预测会威胁生命的心律失常。监视系统应基于对几分钟的心率数据流进行分析来提供诊断。为了验证这种方法的可行性,我们开发了一个原型并评估了其功能。该原型基于两层数据分析方法,并利用了多个梯度提升机器学习模型。仅通过现实的心率读数对系统进行了预测四种不同的危及生命的心律失常的测试,还测试了房颤的识别能力。该原型的准确度分别为91.6%和93.9%。这些初步结果验证了我们从心率观察结果实时预测心律失常方法的可行性。

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