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基于深度学习的可穿戴心冲击图信号到全身冲击波信号的全球化映射模型。

A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning.

出版信息

IEEE J Biomed Health Inform. 2020 May;24(5):1296-1309. doi: 10.1109/JBHI.2019.2931872. Epub 2019 Jul 29.

DOI:10.1109/JBHI.2019.2931872
PMID:31369391
Abstract

The ballistocardiography (BCG) signal is a measurement of the vibrations of the center of mass of the body due to the cardiac cycle and can be used for noninvasive hemodynamic monitoring. The seismocardiography (SCG) signals measure the local vibrations of the chest wall due to the cardiac cycle. While BCG is a more well-known modality, it requires the use of a modified bathroom scale or a force plate and cannot be measured in a wearable setting, whereas SCG signals can be measured using wearable accelerometers placed on the sternum. In this paper, we explore the idea of finding a mapping between zero mean and unit l-norm SCG and BCG signal segments such that, the BCG signal can be acquired using wearable accelerometers (without retaining amplitude information). We use neural networks to find such a mapping and make use of the recently introduced UNet architecture. We trained our models on 26 healthy subjects and tested them on ten subjects. Our results show that we can estimate the aforementioned segments of the BCG signal with a median Pearson correlation coefficient of 0.71 and a median absolute deviation (MAD) of 0.17. Furthermore, our model can estimate the R-I, R-J and R-K timing intervals with median absolute errors (and MAD) of 10.00 (8.90), 6.00 (5.93), and 8.00 (5.93), respectively. We show that using all three axis of the SCG accelerometer produces the best results, whereas the head-to-foot SCG signal produces the best results when a single SCG axis is used.

摘要

心冲击图(BCG)信号是测量由于心脏周期引起的身体质心的振动的一种方法,可以用于无创血流动力学监测。地震心动图(SCG)信号测量由于心脏周期引起的胸壁局部振动。虽然 BCG 是一种更为知名的模态,但它需要使用改装的浴室秤或力板,并且不能在可穿戴设备中进行测量,而 SCG 信号可以使用放置在胸骨上的可穿戴加速度计进行测量。在本文中,我们探讨了在零均值和单位 l-范数 SCG 和 BCG 信号段之间找到映射的想法,以便可以使用可穿戴加速度计获取 BCG 信号(不保留幅度信息)。我们使用神经网络找到这种映射,并利用最近引入的 UNet 架构。我们在 26 位健康受试者上训练我们的模型,并在 10 位受试者上对其进行测试。我们的结果表明,我们可以使用中位数 Pearson 相关系数为 0.71 和中位数绝对偏差(MAD)为 0.17 来估计 BCG 信号的上述段。此外,我们的模型可以估计 R-I、R-J 和 R-K 时间间隔,中位数绝对误差(和 MAD)分别为 10.00(8.90)、6.00(5.93)和 8.00(5.93)。我们表明,使用 SCG 加速度计的所有三个轴都可以产生最佳结果,而当使用单个 SCG 轴时,头到脚的 SCG 信号产生最佳结果。

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