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使用红外视频估算住院患者的呼吸频率:一项观察性研究。

Estimation of respiratory rate using infrared video in an inpatient population: an observational study.

机构信息

Eastern Health Intensive Care Services, Eastern Health, Melbourne, Australia.

School of Public Health and Prevention Medicine, Monash University, Melbourne, Australia.

出版信息

J Clin Monit Comput. 2020 Dec;34(6):1275-1284. doi: 10.1007/s10877-019-00437-2. Epub 2019 Dec 2.


DOI:10.1007/s10877-019-00437-2
PMID:31792761
Abstract

Respiratory rate (RR) is one of the most sensitive markers of a deteriorating patient. Despite this, there is significant inter-observer discrepancy when measured by clinical staff, and modalities used in clinical practice such as ECG bioimpedance are prone to error. This study utilized infrared thermography (IRT) to measure RR in a critically ill population in the Intensive Care Unit. This study was carried out in a Single Hospital Centre. Respiratory rate in 27 extubated ICU patients was counted by two observers and compared to ECG Bioimpedance and IRT-derived RR at distances of 0.4-0.6 m and > 1 m respectively. IRT-derived RR using two separate computer vision algorithms outperformed ECG derived RR at distances of 0.4-0.6 m. Using an Autocorrelation estimator, mean bias was - 0.667 breaths/min. Using a Fast Fourier Transform estimator, mean bias was - 1.000 breaths/min. At distances greater than 1 m no statistically significant signal could be obtained. Over all frequencies, there was a significant relationship between the RR estimated using IRT and via manual counting, with Pearson correlation coefficients between 0.796 and 0.943 (p < 0.001). Correlation between counting and ECG-derived RR demonstrated significance only at > 19 bpm (r = 0.562, p = 0.029). Overall agreement between IRT-derived RR at distances of 0.4-0.6 m and gold standard counting was satisfactory, and outperformed ECG derived bioimpedance. Contactless IRT derived RR may be feasible as a routine monitoring modality in wards and subacute inpatient settings.

摘要

呼吸频率(RR)是评估病情恶化患者的最敏感指标之一。尽管如此,临床医务人员在测量时存在显著的观察者间差异,而临床实践中使用的诸如心电图生物阻抗等方法容易出现误差。本研究利用红外热成像(IRT)技术来测量重症监护病房中危重病患者的 RR。本研究在一家单一医院中心进行。由两名观察者对 27 名已拔管的 ICU 患者的 RR 进行计数,并与心电图生物阻抗和分别距离 0.4-0.6 m 和>1 m 处的 IRT 衍生 RR 进行比较。在距离 0.4-0.6 m 处,使用两种独立的计算机视觉算法的 IRT 衍生 RR 优于心电图衍生 RR。使用自相关估计器,平均偏差为-0.667 次/分钟。使用快速傅里叶变换估计器,平均偏差为-1.000 次/分钟。在距离大于 1 m 处,无法获得具有统计学意义的信号。在所有频率下,使用 IRT 估计的 RR 与手动计数之间存在显著的关系,Pearson 相关系数在 0.796 到 0.943 之间(p<0.001)。计数与心电图衍生 RR 之间的相关性仅在>19 bpm 时具有统计学意义(r=0.562,p=0.029)。距离 0.4-0.6 m 处的 IRT 衍生 RR 与金标准计数之间的总体一致性令人满意,且优于心电图衍生的生物阻抗。非接触式 IRT 衍生 RR 可能作为病房和亚急性住院环境中的常规监测方法成为可行方案。

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引用本文的文献

[1]
Thermal Cameras for Continuous and Contactless Respiration Monitoring.

Sensors (Basel). 2024-12-19

[2]
Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.

Front Bioeng Biotechnol. 2024-7-17

[3]
Uses of infrared thermography in acute illness: a systematic review.

Front Med (Lausanne). 2024-6-24

[4]
Algoritmically improved microwave radar monitors breathing more acurrate than sensorized belt.

Sci Rep. 2022-8-24

[5]
Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.

Sensors (Basel). 2022-5-28

[6]
What is new in respiratory monitoring?

J Clin Monit Comput. 2022-6

[7]
A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars.

Front Physiol. 2022-3-9

[8]
Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection.

Sensors (Basel). 2021-9-21

[9]
Evaluation of respiratory rate monitoring using a microwave Doppler sensor mounted on the ceiling of an intensive care unit: a prospective observational study.

J Clin Monit Comput. 2022-2

[10]
A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.

Sensors (Basel). 2021-2-21

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