Contactless respiration rate measurement using optical method and empirical mode decomposition
K B Gan1, E S Yahyavi1, M S Ismail2
1Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Summary
This study developed a contactless displacement sensor and Empirical Mode Decomposition (EMD) algorithm to accurately measure respiration rate. The contactless method shows high correlation with traditional methods, overcoming challenges in emergency triage.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Medical Devices
Background:
- Respiration rate measurement is crucial in emergency triage but conventional methods are challenging.
- Current techniques are impractical, time-consuming, and can cause patient stress or cultural issues.
- Visual counting of chest movements is subjective and poses specific challenges.
Purpose of the Study:
- To develop a robust algorithm for extracting respiration rate using a contactless displacement sensor.
- To provide a non-invasive and accurate method for respiratory rate monitoring.
- To address the limitations of current respiration rate assessment tools.
Main Methods:
- Chest movements were utilized as indicators of respiration using a contactless optical displacement sensor.
- Contactless optical signals were recorded from 32 healthy subjects under various breathing conditions.
- Empirical Mode Decomposition (EMD) algorithm was employed to derive the respiration rate (RR) signal.
Main Results:
- A strong correlation (0.9702) was observed between contact and contactless respiration rate measurements.
- The Root Mean Square Error (RMSE) was 0.33 breaths per minute, indicating high accuracy.
- The EMD method effectively extracted respiratory rate from contactless optical signals, even with motion artifacts.
Conclusions:
- The Empirical Mode Decomposition (EMD) method is effective for extracting respiration rate from contactless optical signals.
- This contactless approach offers a promising solution for accurate respiratory rate monitoring in clinical settings.
- The developed algorithm can overcome the practical challenges associated with traditional respiration rate measurement devices.
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