A domain adversarial network for small-sample obstructive sleep apnea detection using a single-lead piezoelectric sensor [0.03%]
一种用于单导压电传感器的小样本睡眠呼吸暂停检测的领域对抗网络方法
Xuefeng Song,Xiaoxin Lan,Tianyuan Hou et al.
Xuefeng Song et al.
Objective.In recent years, increasing attention to sleep health has accelerated development of wearable devices for home monitoring. Two critical challenges remain: providing an unobtrusive sensing solution suitable for long-term deployment...
Deep learning based automated assessment of end-inspiratory pause maneuver reliability in invasive mechanical ventilation [0.03%]
基于深度学习的侵入性机械通气下终末吸气停顿试验可靠性的自动评估方法研究
Qing Pan,Haifeng Su,Lingwei Zhang et al.
Qing Pan et al.
Objective: Manual end-inspiratory pause maneuvers (EIPM) for plateau pressure (Pplat) measurement suffer from significant variability, yet no objective assessment tool exists. This study develops an automated framework to...
Minimum data length required for reliable resting frequency-domain ultra-short-term heart rate variability analysis [0.03%]
基于静息状态下的频域超短时心率变异性分析所需最小数据长度研究
Xiangni Lin,Gengxing Liu,Lin Wang et al.
Xiangni Lin et al.
Objective: Heart rate variability (HRV) is widely used to assess autonomic function, but the minimum data length required for reliable ultra-short-term frequency-domain analysis remains without consensus. This study aimed...
Deep learning approach for stroke volume variation estimation: retrospective modeling and prospective deployment in real-world practice [0.03%]
深度学习在脑卒中患者血流量变异估计中的应用：回顾性建模与前瞻性分析
Jae-Man Shin,Woo-Young Seo,Woo-Jin Kim et al.
Jae-Man Shin et al.
Objective. We developed stroke volume variation (SVV) Net, a deep learning-based model for estimating SVV, and validated its performance and clinical applicability through both large-scale retrospective modeling and prospective real-time de...
Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads [0.03%]
基于电容传感垫的心血管参数和脉波传导时间的连续血压监测法
Yu-Jen Cheng,Edward Kim,Jin-Oh Hahn et al.
Yu-Jen Cheng et al.
Objective.Continuous blood pressure (BP) monitoring is crucial for detecting nocturnal hypertension and acute hemodynamic changes. Conventional cuff-based methods disrupt sleep and miss transient BP fluctuations from sleep-related events or...
NeuroSleep: neuromorphic event-driven single-channel EEG sleep staging for edge-efficient sensing [0.03%]
神经睡眠：事件驱动单通道EEG睡眠分期的类脑感知技术
Boyu Li,Xingchun Zhu,Yonghui Wu
Boyu Li
Objective: Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is...
Towards fair and trustworthy heart rate estimation from wrist-worn photoplethysmography: a multi-wavelength dataset and uncertainty-aware deep learning approach evaluated across skin tones, sexes, and motion conditions [0.03%]
面向公平和可信的手腕光电容积描记法心率估计：一个跨肤色、性别和运动条件评估的多波长数据集和不确定性感知深度学习方法
Daniel Ray,Tim Collins,Prasad V S Ponnapalli
Daniel Ray
Objective: To improve fairness (reduced disparities across skin tones and sexes) and trust (well-calibrated uncertainty metrics that indicate unreliable predictions) in wrist-worn photoplethysmography (PPG) heart rate est...
StructEIT: Realistic 3D EIT model generation from CT scans for deep learning applications [0.03%]
基于CT扫描的 realistic 3D EIT 模型生成用于深度学习应用
Zeyi Jiang,Sirui Qiao,Chuanbao Wu et al.
Zeyi Jiang et al.
Objective: Artificial intelligence (AI) has significantly improved image reconstruction quality across various medical imaging modalities. However, its application in electrical impedance tomography (EIT) reconstruction r...
Non-invasive hemodynamic monitoring during hemorrhage and blood transfusion: Opportunities and challenges [0.03%]
出血与输血时的无创性血液动力学监测：机遇与挑战
Parham Rezaei,Sina Masoumi Shahrbabak,John Vandenberge et al.
Parham Rezaei et al.
Objective: We investigated (i) if blood volume decompensation status (BVDS) can be trend-tracked by hemodynamic parameters, and (ii) if hemodynamic parameters capable of trend-tracking BVDS can be trend-tracked by the phy...
Towards real-time sleep stage classification: A deep learning approach leveraging PPG and ECG [0.03%]
面向实时睡眠分期：一种利用PPG和ECG的深度学习方法
Shagen Djanian,Thomas Dyhre Nielsen,Søren H Nielsen et al.
Shagen Djanian et al.
Objective: This work aims to enable adaptive Consumer Sleep Technologies (CSTs) for sleep intervention by developing a deep learning model for sleep stage classification using wearable sensor data. ...