论文标题
深度转移学习以改善单eeg唤醒检测
Deep transfer learning for improving single-EEG arousal detection
论文作者
论文摘要
由于诊所的记录设置差异，睡眠科学中的数据集对机器学习算法提出了挑战。我们研究了两个深层转移学习策略，以克服两个数据集不包含完全相同的设置的情况，从而导致单eeg模型中的性能降低的情况。具体而言，我们在多元多元术数据上训练基线模型，然后替换前两层，以准备单渠道脑电图数据的体系结构。使用微调策略，我们的模型产生的性能与基线模型相似（分别为F1 = 0.682和F1 = 0.694），并且明显优于类似的单通道模型。对于希望使用在较大数据库中预先训练的深度学习模型的研究人员工作的研究人员来说，我们的结果很有希望。
Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.
