Study Objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature. Methods: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography (PSG) at a tertiary care sleep lab. Each PSG recording was manually scored and these annotations served as ground truth labels for training and evaluation of our models. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. Results: After ensembling, the model attains a 3-class (wake, non rapid eye movement [NREM] sleep, rapid eye movement [REM] sleep) balanced accuracy of 84.02%, F1 score of 84.23%, Cohen's $κ$ of 72.89%, and a Matthews correlation coefficient (MCC) score of 73.00%; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30%, F1 score of 74.10%, Cohen's $κ$ of 61.51%, and MCC score of 61.95%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11%, F1 score of 66.15%, Cohen's $κ$ of 53.23%, MCC score of 54.38%. Conclusions: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.

翻译：研究目的：本研究探讨一种基于Mamba的深度学习方法，用于对来自ANNE One（Sibel Health，埃文斯顿，伊利诺伊州）信号的睡眠分期。ANNE One是一种非侵入性双模块无线可穿戴系统，可测量胸部心电图（ECG）、三轴加速度计、胸部温度，以及手指光电容积描记法和手指温度。方法：我们获取了357名成年人在三级护理睡眠实验室同时进行多导睡眠图（PSG）监测时的可穿戴传感器记录。每份PSG记录均经过人工评分，这些注释作为我们模型训练和评估的真实标签。PSG与可穿戴传感器数据通过其ECG通道自动对齐，并经过人工视觉检查确认。我们基于这些记录训练了一个基于Mamba的循环神经网络架构。并对具有相似架构的模型变体进行了集成。结果：集成后，模型在3分类（清醒、非快速眼动[NREM]睡眠、快速眼动[REM]睡眠）上达到平衡准确率84.02%，F1分数84.23%，Cohen's $κ$ 72.89%，马修斯相关系数（MCC）得分73.00%；在4分类（清醒、浅度NREM睡眠[N1/N2]、深度NREM睡眠[N3]、REM）上达到平衡准确率75.30%，F1分数74.10%，Cohen's $κ$ 61.51%，MCC得分61.95%；在5分类（清醒、N1、N2、N3、REM）上达到平衡准确率65.11%，F1分数66.15%，Cohen's $κ$ 53.23%，MCC得分54.38%。结论：我们基于Mamba的深度学习模型能够成功地从ANNE One（一种无脑电图[EEG]的可穿戴系统）推断主要睡眠阶段，并可应用于来自三级护理睡眠诊所的成人数据。