研究目的
Assessing the effectiveness of infrared thermography (IRT) as a modality to measure respiratory rate (RR) in critically ill patients compared to manual counting and ECG bioimpedance, and evaluating its accuracy across different respiratory frequencies.
研究成果
IRT-based measurements can accurately identify respiratory rates in critically ill patients, outperforming ECG bioimpedance. The technology shows promise for contactless and unobtrusive monitoring of respiratory rate in clinical settings, though further studies are needed to validate these results and improve practicality.
研究不足
The study was performed on a small number of patients in a controlled setting. Patients knew they were being recorded, which may have affected their breathing patterns. The camera only maintained accuracy at a close distance (40–60 cm), which may not be practical for daily use. Automated body part tracking algorithms for IR-based cameras are poorly characterized.
1:Experimental Design and Method Selection:
The study utilized infrared thermography (IRT) to measure RR in critically ill patients in the ICU, comparing it to manual counting and ECG bioimpedance. Two computer vision algorithms (Autocorrelation and Fast Fourier Transform) were used for RR estimation.
2:Sample Selection and Data Sources:
27 extubated ICU patients were included. Respiratory rate was manually counted by two observers and compared to ECG Bioimpedance and IRT-derived RR at distances of 0.4–0.6 m and > 1 m.
3:4–6 m and > 1 m.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Optris PI 450 infrared camera with a standard O29 lens, Philips Intellivue MX800 bedside monitor for ECG bioimpedance.
4:Experimental Procedures and Operational Workflow:
Thermal videos of the patients' faces were recorded at two distances. The first 30 s of a minimum 40 s recording was used for analysis. Patients were asked to refrain from talking during recording.
5:Data Analysis Methods:
Frame-by-frame temperature data were extracted and analyzed using MATLAB. Respiratory rate was estimated using autocorrelation and Fast Fourier Transform (FFT) algorithms.
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