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Contactless Sensing-Aided Respiration Signal Acquisition Using Improved Empirical Wavelet Transform for Rhythm
IEEE Journal of Biomedical and Health Informatics
|April 28, 2023
Summary
This study introduces a contactless method using improved empirical wavelet transform (IEWT) to accurately detect respiration signals, even with varying rhythms. The IEWT method shows high accuracy in both lab and clinical settings for vital sign monitoring.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Physiological Monitoring
Background:
- Respiration is a critical vital sign, but its accurate detection is challenging due to complex rhythms and interference.
- Contactless methods offer a non-invasive approach to physiological monitoring, reducing patient discomfort and infection risk.
- Existing techniques struggle with time-varying respiration patterns and interference from other physiological signals.
Purpose of the Study:
- To develop and validate a contactless respiration signal acquisition technique.
- To adaptively extract respiration signals under diverse and time-varying conditions.
- To improve the accuracy and robustness of respiration monitoring compared to conventional methods.
Main Methods:
- Utilized piezoelectric ceramic sensors for contactless respiration perception, capturing signals alongside ballistocardiography and other interferences.
- Developed an improved empirical wavelet transform (IEWT) for adaptive spectrum division and recognition.
- Employed upper envelope and principal component criteria for accurate respiration spectrum extraction and signal reconstruction.
Main Results:
- Achieved a correlation coefficient of 0.98 for respiration rhythms in lab settings, validating the IEWT method's effectiveness.
- Demonstrated low mean absolute (0.4s) and relative (0.05s) errors in respiration intervals for clinical sleep-disordered breathing data.
- Showcased robustness of IEWT to rhythm variations, individual differences, and various breathing detection techniques.
Conclusions:
- The proposed contactless sensing-aided IEWT method effectively extracts respiration signals with high accuracy.
- IEWT offers significant improvements over conventional methods for practical respiration monitoring in clinical scenarios.
- The technique is feasible and superior for real-world applications requiring reliable vital sign detection.
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