An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN
An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN
复制标题
使用 CycleGAN 的精确非基于加速度计的 PPG 运动伪影去除技术
DOI：
10.1145/3563949
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kurdahi, Fadi
中科院分区：
文献类型：
--
作者：
Zargari, Amir Hosein;Aqajari, Seyed Amir;Khodabandeh, Hadi;Rahmani, Amir M.;Kurdahi, Fadi
体积描记(PPG)是一种简单、廉价的光学技术，广泛应用于医疗保健领域，用于提取与健康相关的有价值的信息，如心率变异性、血压和呼吸频率。使用便携式可穿戴设备，可以轻松地连续和远程采集ppg信号。然而，这些测量设备容易受到日常生活活动造成的运动伪影的影响。消除运动伪影的最常见方法是使用额外的加速度计传感器，这受到两个限制：(I)高功耗，和(Ii)需要在可穿戴设备中集成加速度计传感器(这在某些可穿戴设备中不是必需的)。本文提出了一种基于无加速度计的低功耗PPG运动伪影去除方法，其精度优于现有方法。我们使用循环生成对抗网络从有噪声的PPG信号中重建干净的PPG信号。我们基于机器学习的新技术在不使用额外传感器(如加速度计)的情况下，在运动伪影去除方面比最先进的技术提高了9.5倍，从而使能效提高了45%。
Aphotoplethysmography (PPG)is an uncomplicated and inexpensive optical technique widely used in the healthcare domain to extract valuable health-related information, e.g., heart rate variability, blood pressure, and respiration rate. PPG signals can easily be collected continuously and remotely using portable wearable devices. However, these measuring devices are vulnerable to motion artifacts caused by daily life activities. The most common ways to eliminate motion artifacts use extra accelerometer sensors, which suffer from two limitations: (i) high power consumption, and (ii) the need to integrate an accelerometer sensor in a wearable device (which is not required in certain wearables). This paper proposes a low-power non-accelerometer-based PPG motion artifacts removal method outperforming the accuracy of the existing methods. We use Cycle Generative Adversarial Network to reconstruct clean PPG signals from noisy PPG signals. Our novel machine-learning-based technique achieves 9.5 times improvement in motion artifact removal compared to the state-of-the-art without using extra sensors such as an accelerometer, which leads to 45% improvement in energy efficiency.
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DOI：
10.1109/icassp40776.2020.9053865
发表时间：
2020
期刊：
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影响因子：
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作者：
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