Study Objectives: We investigate using Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a minimally intrusive dual-sensor wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and temperature, as well as finger photoplethysmography (PPG) and temperature. Methods: We obtained wearable sensor recordings from 360 adults undergoing concurrent clinical polysomnography (PSG) at a tertiary care sleep lab. PSG recordings were scored according to AASM criteria. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained Mamba-based models with both convolutional-recurrent neural network (CRNN) and the recurrent neural network (RNN) architectures on these recordings. Ensembling of model variants with similar architectures was performed. Results: Our best approach, after ensembling, attains a 3-class (wake, NREM, REM) balanced accuracy of 83.50%, F1 score of 84.16%, Cohen's $\kappa$ of 72.68%, and a MCC score of 72.84%; a 4-class (wake, N1/N2, N3, REM) balanced accuracy of 74.64%, F1 score of 74.56%, Cohen's $\kappa$ of 61.63%, and MCC score of 62.04%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 64.30%, F1 score of 66.97%, Cohen's $\kappa$ of 53.23%, MCC score of 54.38%. Conclusions: Deep learning models can infer major sleep stages from a wearable system without electroencephalography (EEG) and can be successfully applied to data from adults attending a tertiary care sleep clinic.

翻译：研究目标：我们研究使用基于Mamba的深度学习方法，对来自ANNE One（Sibel Health，埃文斯顿，伊利诺伊州）的信号进行睡眠分期。ANNE One是一种微创双传感器无线可穿戴系统，可测量胸部心电图（ECG）、三轴加速度计和温度，以及手指光电容积脉搏波（PPG）和温度。方法：我们获取了360名成年人在三级护理睡眠实验室同时进行临床多导睡眠图（PSG）监测时的可穿戴传感器记录。PSG记录根据AASM标准进行评分。PSG和可穿戴传感器数据通过其ECG通道自动对齐，并通过目视检查进行手动确认。我们使用卷积循环神经网络（CRNN）和循环神经网络（RNN）架构在这些记录上训练了基于Mamba的模型。并对具有相似架构的模型变体进行了集成。结果：我们集成后的最佳方法，在3类（清醒、NREM、REM）分期中达到了83.50%的平衡准确率、84.16%的F1分数、72.68%的Cohen's $\kappa$和72.84%的MCC分数；在4类（清醒、N1/N2、N3、REM）分期中达到了74.64%的平衡准确率、74.56%的F1分数、61.63%的Cohen's $\kappa$和62.04%的MCC分数；在5类（清醒、N1、N2、N3、REM）分期中达到了64.30%的平衡准确率、66.97%的F1分数、53.23%的Cohen's $\kappa$和54.38%的MCC分数。结论：深度学习模型可以从无脑电图（EEG）的可穿戴系统推断主要睡眠阶段，并可以成功应用于在三级护理睡眠诊所就诊的成年人的数据。