无需脑电图，用可穿戴设备+魔改Mamba模型自动判别睡眠阶段。
Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
- 用Mamba架构处理多模态可穿戴信号，替代传统脑电图。
- 三类睡眠阶段准确率达84.02%，五类达65.11%。
- 适合临床睡眠监测场景，无需侵入式设备。
本研究评估了一种基于Mamba的深度学习方法，在无脑电图（EEG）条件下通过ANNE One可穿戴系统（Sibel Health）采集的胸腔心电图（ECG）、三轴加速度、胸温、指端光体积脉搏波和指温信号进行睡眠分期。在357名接受多导睡眠图（PSG）同步监测的成人中获取数据，以人工标注的PSG结果为标签训练与评估模型。利用心电信号自动对齐并经人工视觉确认后，构建并训练了基于Mamba的循环神经网络。通过同构模型集成，三分类（清醒、非快速眼动[非快动眼]、快速眼动）平衡准确率为84.02%，F1得分为84.23%，Cohen's κ为72.89%，马修相关系数（MCC）为73.00%；四分类（清醒、浅睡[N1/N2]、深睡[N3]、REM）平衡准确率75.30%，F1得分74.10%，κ为61.51%，MCC为61.95%；五分类（清醒、N1、N2、N3、REM）平衡准确率65.11%，F1得分66.15%，κ为53.23%，MCC为54.38%。结果表明，该模型能有效从无EEG的可穿戴设备中推断主要睡眠阶段，并适用于三级医疗睡眠中心人群。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。