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一种用于跟踪肺部液体状态、肺部声音和呼吸标志物变化的可穿戴多模态传感系统。

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers.

机构信息

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA.

Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

出版信息

Sensors (Basel). 2022 Feb 2;22(3):1130. doi: 10.3390/s22031130.

Abstract

Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuous, holistic measures of cardiopulmonary health. We present a multimodal sensing system that captures bioimpedance spectroscopy (BIS), multi-channel lung sounds from four contact microphones, multi-frequency impedance pneumography (IP), temperature, and kinematics to track changes in cardiopulmonary status. We first validated the system on healthy subjects ( = 10) and then conducted a feasibility study on patients ( = 14) with HF in clinical settings. Three measurements were taken throughout the course of hospitalization, and parameters relevant to lung fluid status-the ratio of the resistances at 5 kHz to those at 150 kHz ()-and respiratory timings (e.g., respiratory rate) were extracted. We found a statistically significant increase in ( < 0.05) from admission to discharge and observed respiratory timings in physiologically plausible ranges. The IP-derived respiratory signals and lung sounds were sensitive enough to detect abnormal respiratory patterns (Cheyne-Stokes) and inspiratory crackles from patient recordings, respectively. We demonstrated that the proposed system is suitable for detecting changes in pulmonary fluid status and capturing high-quality respiratory signals and lung sounds in a clinical setting.

摘要

心力衰竭(HF)恶化的特征是肺部充血和呼吸困难,需要频繁住院,通常导致不良后果。目前用于跟踪肺部液体和呼吸窘迫的方法无法提供心肺健康的连续、整体测量。我们提出了一种多模态传感系统,该系统可以捕获生物阻抗光谱(BIS)、来自四个接触麦克风的多通道肺部声音、多频阻抗肺图(IP)、温度和运动学,以跟踪心肺状态的变化。我们首先在健康受试者(n = 10)上验证了该系统,然后在临床环境中对心力衰竭患者(n = 14)进行了可行性研究。在整个住院过程中进行了三次测量,并提取了与肺部液体状态相关的参数——5 kHz 处的电阻与 150 kHz 处的电阻之比()——和呼吸时间(例如,呼吸频率)。我们发现从入院到出院,呈统计学显著增加(<0.05),并且观察到生理上合理范围内的呼吸时间。IP 衍生的呼吸信号和肺部声音足够灵敏,可以分别检测到来自患者记录的异常呼吸模式(Cheyne-Stokes)和吸气爆裂声。我们证明了所提出的系统适用于检测肺部液体状态的变化,并在临床环境中捕获高质量的呼吸信号和肺部声音。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d275/8838360/0fb8ce11666d/sensors-22-01130-g001.jpg

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