Department of Electrical and Computer Engineering Capstone, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
Djavad Mowafaghian Centre for Brain Health, Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Sensors (Basel). 2021 May 11;21(10):3316. doi: 10.3390/s21103316.
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.
睡眠障碍在阿尔茨海默病和其他神经退行性疾病中很常见，共同代表了疾病修饰的潜在治疗靶点。研究痴呆患者睡眠的一个主要障碍是需要进行整夜多导睡眠图（PSG）以实现正式的睡眠分期。这不仅成本高昂，而且让患者在医院环境中过夜并不总是明智的。作为 PSG 的替代方案，便携式脑电图（EEG）头带（HB）已经开发出来，它降低了成本，提高了患者舒适度，并允许在患者的家庭环境中进行睡眠记录。然而，由于其相对较低的质量，当前的自动化睡眠分期系统的盲目应用往往不能很好地应用于 HB 数据。在这里，我们提出了一种用于 HB EEG 数据自动化睡眠分期的深度学习（DL）模型，以克服这些关键限制。该解决方案包括简单的带通滤波、数据增强步骤以及使用卷积（CNN）和长短期记忆（LSTM）层的模型。使用该模型，我们在低质量的双通道 EEG 头带数据上实现了 74%（±10%）的验证准确性，在黄金标准 PSG 上实现了 77%（±10%）的验证准确性。我们的结果表明，DL 方法可以稳健地对便携式和住院 EEG 记录进行睡眠分期，并且可能允许在更广泛的临床条件下更广泛地使用动态睡眠评估，包括神经退行性疾病。
