Hear the heart: Daily cardiac health monitoring using Ear-ECG and machine learning

机译：聆听心脏：使用Ear-ECG和机器学习进行每日心脏健康监测

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Daily cardiac health monitoring is of high importance for effective heart disease prediction/management. In this study, we propose a novel ear-worn system for long-term continuous ECG QRS duration tracking, to overcome challenges of current wearable ECG systems such as the uncomfortableness and inconvenience. Specifically, we place all the ECG electrodes behind the ear to enhance the wearability, and weakoisy ear-ECG is obtained. Then, we use a support vector machine classifier for heartbeat identification, apply an unsupervised learning approach for heartbeat purification, and a regression model to derive the standard chest-ECG QRS durations from the ear-ECG QRS durations. We have evaluated the proof-of-concept system using an ear-ECG dataset acquired by a semi-customized wearable prototype, and demonstrated the effectiveness of the proposed system. To the best of our knowledge, it is the first study on an ear-worn system for ECG QRS duration estimation, which can be used in daily cardiac health monitoring applications.

机译：每日心脏健康监测对于有效的心脏病预测/治疗非常重要。在这项研究中，我们提出了一种用于长期连续ECG QRS持续时间跟踪的新型耳戴系统，以克服当前可穿戴ECG系统的挑战，例如不适和不便。具体来说，我们将所有的ECG电极放置在耳朵后面以增强可穿戴性，从而获得弱/嘈杂的耳朵ECG。然后，我们使用支持向量机分类器进行心跳识别，应用无监督学习方法进行心跳净化，并使用回归模型从耳朵ECG QRS时长中得出标准胸部ECG QRS时长。我们使用半定制可穿戴原型获得的耳心电图数据集评估了概念验证系统，并证明了该系统的有效性。据我们所知，这是首次用于ECG QRS持续时间估计的耳戴式系统研究，该系统可用于日常心脏健康监测应用中。

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《IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference》|2017年|448-451|共4页
会议地点 New York(US)
作者
Qingxue Zhang; Dian Zhou; Xuan Zeng;
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作者单位

University of Texas at Dallas USA;

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原文格式 PDF
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关键词
Electrocardiography; Heart beat; Monitoring; Biomedical monitoring; Diseases; Purification;

机译：心电图；心跳监控；生物医学监测；疾病；纯化;

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Hear the heart: Daily cardiac health monitoring using Ear-ECG and machine learning

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