Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3546-3549. doi: 10.1109/EMBC48229.2022.9871268.
Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine learning models is the requirement of huge datasets. In the biomedical domain, this becomes extremely difficult due to uncertainties in collecting high-quality data as well as, in the case of human subjects data, privacy. Further, when it comes to biomedical data, inter-subject variability has been a long-entrenched issue. The data obtained from different individuals will differ to a considerable extent that it becomes difficult to find population differences in small datasets. In this work, we investigate the use of label alignment techniques on an EEG-based Traumatic Brain Injury (TBI) classification task to overcome inter-subject variability, thereby increasing the classification accuracy. We show an increase in accuracy of around 6% in some cases as compared to our previous results. In the end, we also propose a methodology to incorporate TBI data from a different species (e.g., mice) after domain adaptation, which might further improve the performance by increasing the amount of training datasets available for the classification model.
机器学习和深度学习算法为生物医学数据的分析提供了改进的方法，从而更好地理解了各种生物条件。然而，利用机器学习模型的潜力的一个主要障碍是需要大量数据集。在生物医学领域，由于收集高质量数据的不确定性以及在人类受试者数据的情况下的隐私问题，这变得极其困难。此外，当涉及到生物医学数据时，个体间的可变性一直是一个长期存在的问题。从不同个体获得的数据会有很大的差异，以至于在小数据集上很难找到群体差异。在这项工作中，我们研究了在基于脑电图的创伤性脑损伤 (TBI) 分类任务中使用标签对齐技术来克服个体间的可变性，从而提高分类准确性。与我们之前的结果相比，在某些情况下，准确性提高了约 6%。最后，我们还提出了一种在经过领域适应后将来自不同物种（例如，老鼠）的 TBI 数据纳入的方法，通过增加分类模型可用的训练数据集的数量，这可能会进一步提高性能。
