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可穿戴实时心脏病发作检测和预警系统,减少道路事故。

Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents.

机构信息

Electrical Engineering Department, College of Engineering, Qatar University, Doha-2713, Qatar.

Department of Industrial and Mechanical Engineering, College of Engineering, Qatar University, Doha 2713, Qatar.

出版信息

Sensors (Basel). 2019 Jun 20;19(12):2780. doi: 10.3390/s19122780.

Abstract

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone-that is one in every four deaths-but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time-frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

摘要

心脏病发作是全球导致人类死亡的主要原因之一。仅在美国,每年就有约 61 万人死于心脏病发作——也就是说,每四个人中就有一人死亡——但心脏病发作有一些早期症状是可以被理解的,如果能及早发现和报告,就可以极大地帮助挽救许多生命并将损害降到最低。另一方面,每年约有 235 万人因道路交通事故受伤或残疾。出乎意料的是,许多致命事故是由于司机心脏病发作导致车辆失控而发生的。目前的工作提出了开发一种用于实时检测和警告驾驶员心脏病发作的可穿戴系统,这对于减少道路交通事故将有很大帮助。该系统由两个使用蓝牙技术无线通信的子系统组成,即可穿戴式传感器子系统和智能心脏病发作检测和警告子系统。传感器子系统从胸部区域记录心脏的电活动,以产生心电图(ECG)轨迹,并将其发送到另一个便携式决策子系统,在该子系统中检测心脏病发作的症状。我们评估了干电极和不同电极配置的性能,并测量了系统的总功耗。训练和测试了线性分类和几种机器算法,以实现实时应用。结果表明,线性分类算法无法在噪声数据中检测到心脏病发作,而使用扩展修正 B 分布(EMBD)的多项式核扩展时频特征的支持向量机(SVM)算法显示出最高的准确性,能够分别检测到 97.4%和 96.3%的 ST 段抬高型心肌梗死(STEMI)和非 ST 段抬高型心肌梗死(NSTEMI)。因此,该系统有助于减少全球日益增多的道路交通事故造成的生命损失。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5a1/6632021/fb983b8db544/sensors-19-02780-g001.jpg

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