Nahmias David O, Kontson Kimberly L
Office of Science and Engineering Laboratories, Division of Biomedical Physics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States.
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States.
Front Neurosci. 2021 Feb 12;15:566004. doi: 10.3389/fnins.2021.566004. eCollection 2021.
With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets ( = 60 subjects) to score EEG quality based on the presence of ocular artifacts with our unimodal method and motion artifacts with our multimodal method. Further, we apply our unimodal source method to compare the performance of two different artifact removal algorithms. Our results show we are able to effectively score EEG data using both methods and apply our method to evaluate the performance of other artifact removal algorithms that target ocular artifacts. Methods developed and validated here can be used to assess data quality and evaluate the effectiveness of certain noise-reduction algorithms.
随着从便携式非侵入性方式在实验室外收集的电生理数据的普及率迅速增长，如果这些记录数据的质量不达标，可能会影响依赖它们的医疗设备的有效性。在这项工作中，我们提出了新颖的方法来评估电生理信号质量，以确定有多少数据代表了感兴趣的生理来源。通过贝叶斯决策和基于深度学习的方法分别研究数据驱动模型，以对单峰（在同一设备上记录的信号和噪声）和多峰（分别从不同设备记录的信号和噪声）数据进行评分。我们在三个脑电图（EEG）数据集（n = 60名受试者）上验证了这些方法和模型，使用我们的单峰方法根据眼动伪迹的存在情况对EEG质量进行评分，使用多峰方法根据运动伪迹的存在情况对EEG质量进行评分。此外，我们应用单峰源方法比较两种不同伪迹去除算法的性能。我们的结果表明，我们能够使用这两种方法有效地对EEG数据进行评分，并应用我们的方法评估针对眼动伪迹的其他伪迹去除算法的性能。这里开发和验证的方法可用于评估数据质量和评估某些降噪算法的有效性。
