Biometrics and Healthcare System Using EMG and ECG Signals

Yeong-Hyeon Byeon, S. Pan, Keun-Chang Kwak
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Abstract

In this paper, we study biometrics and healthcare system using electromyogram (EMG) and electrocardiogram (ECG) signals. Most people use smart devices for convenient life and they should be authenticated to access their devices. In this course, people should give their bio-signals to the devices inevitably. Here we use EMG signal for biometrics and ECG signal for heart disease detection. The EMG and ECG signals are a bio-signal that measures the potential difference generated by muscles and heart through the skin of the body surface, respectively. Using the EMG signal for biometrics improves the security of visual exposure. However, the EMG signals show various shapes by muscular strength and which muscles are used. For designing robust biometrics using EMG signal, a set of useful features is extracted and the features enter long short-term memory for training the network. For heart disease detection using ECG signals the signals are transformed into an image and the image is used to train a convolutional neural network. Publicly opened datasets of EMG and ECG signals are used for the experiment and the performance is compared by some parameter changes.
使用肌电图和心电信号的生物识别和医疗保健系统
在本文中,我们研究生物识别和医疗保健系统使用肌电图(EMG)和心电图(ECG)信号。大多数人使用智能设备是为了方便生活,他们应该通过身份验证才能访问他们的设备。在这个过程中,人们不可避免地要将自己的生物信号传递给设备。在这里,我们使用肌电信号进行生物识别,使用心电信号进行心脏病检测。肌电图和心电信号是通过体表皮肤分别测量肌肉和心脏产生的电位差的生物信号。使用肌电图信号进行生物识别可以提高视觉暴露的安全性。然而,肌电图信号显示不同形状的肌肉力量和哪些肌肉被使用。为了利用肌电信号设计鲁棒性生物识别,提取一组有用的特征,并将这些特征输入长短期记忆中训练神经网络。利用心电信号检测心脏病时,将信号转换成图像,然后用图像训练卷积神经网络。实验使用公开开放的肌电信号和心电信号数据集,并通过一些参数的变化来比较性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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