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Movement artefact removal from NIRS signal using multi-channel IMU data
Masudur R Siddiquee1, J Sebastian Marquez2, Roozbeh Atri2
1Florida International University, Miami, FL, 33174, USA. msidd021@fiu.edu.
Biomedical Engineering Online
|September 12, 2018
Summary
Movement artifacts in near-infrared spectroscopy (NIRS) signals are reduced by using data from an inertia measurement unit (IMU) with accelerometer, gyroscope, and magnetometer. This integrated sensor approach improves signal-to-noise ratio and enhances blood oxygenation measurements during movement.
Area of Science:
- Biomedical Engineering
- Optical Imaging
Background:
- Near-infrared spectroscopy (NIRS) is a non-invasive technique for measuring blood oxygenation.
- Movement artifacts are a significant challenge in NIRS data acquisition.
- Inertia Measurement Units (IMUs) offer potential for artifact correction.
Purpose of the Study:
- To evaluate the effectiveness of a multi-sensor IMU for removing movement artifacts in NIRS.
- To compare artifact removal performance using different combinations of IMU sensors.
- To enhance the accuracy of blood oxygenation measurements during subject movement.
Main Methods:
- Developed a wearable two-channel continuous wave NIRS system integrated with an IMU (accelerometer, gyroscope, magnetometer).
- Simulated and recorded natural movement artifacts during NIRS signal acquisition.
- Applied autoregressive with exogenous input modeling using IMU data to estimate and remove artifacts.
- Assessed performance by Signal-to-Noise Ratio (SNR) improvement and stability of HbO2/Hb levels.
Main Results:
- Utilizing accelerometer, gyroscope, and magnetometer data from the IMU significantly improved SNR by 5-11 dB compared to accelerometer-only data.
- The combined sensor approach provided superior artifact estimation and removal during natural movements.
- Minimal changes in HbO2 and Hb levels were observed during artifact removal, indicating successful correction.
Conclusions:
- Integrated IMU sensors (accelerometer, gyroscope, magnetometer) enable more accurate estimation and removal of movement artifacts in NIRS signals.
- This multi-sensor approach enhances the reliability of NIRS for blood oxygenation monitoring in dynamic conditions.
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