Zia Jonathan, Kimball Jacob, Inan Omer T
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:473-476. doi: 10.1109/EMBC44109.2020.9176732.
Captured with a chest-mounted sensor, the seismo- cardiogram (SCG) is a useful signal for assessing cardiomechanical function. However, the reliability of information obtained from this signal often depends upon sensor location. This has important practical implications, as consistent placement is not guaranteed in at-home and other uncontrolled settings. Building on prior research that localized SCG sensor placement when the patient was at rest - which may not be the case in practical settings - this work presents a more robust method which is able to localize sensor placement during dynamic periods, specifically exercise recovery. This was accomplished via a template-based signal quality index (SQI), which was used to infer sensor location using a variety of classifiers. While prior work generated synthetic templates for this task using an averaging method, it is shown that selecting representative templates from the training set instead enables, for the first time, SCG sensor localization during dynamic periods without patient-specific calibration. With this method, a peak accuracy of 83.32% was achieved for correctly classifying sensor position among five tested positions, with avenues for improvement of these results also presented.
地震心动图（SCG）通过胸部佩戴的传感器采集，是评估心脏机械功能的有用信号。然而，从该信号获取的信息的可靠性通常取决于传感器的位置。这具有重要的实际意义，因为在家庭和其他不受控制的环境中无法保证一致的放置位置。基于先前在患者休息时对SCG传感器放置位置进行定位的研究——而在实际环境中可能并非如此——这项工作提出了一种更强大的方法，该方法能够在动态时期，特别是运动恢复期间定位传感器的放置位置。这是通过基于模板的信号质量指数（SQI）来实现的，该指数用于使用各种分类器推断传感器的位置。虽然先前的工作使用平均方法为该任务生成合成模板，但结果表明，从训练集中选择代表性模板首次能够在无需特定患者校准的情况下在动态时期进行SCG传感器定位。使用这种方法，在五个测试位置中正确分类传感器位置的峰值准确率达到了83.32%，同时还提出了改进这些结果的途径。
