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Detrending knee joint vibration signals with a cascade moving average filter
Suxian Cai1, Yunfeng Wu, Ning Xiang
1Department of Communication Engineering, School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian, 361005, China.
Summary
This study introduces a novel cascade moving average filter to eliminate baseline wander in knee joint vibration signals. The new filter effectively removes signal noise caused by patient discomfort during medical analysis.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Orthopedics
Background:
- Knee joint vibration signals are crucial for computer-aided diagnosis of knee pathologies.
- Patient leg tremors during vibration arthrometry can cause baseline wander, impacting diagnostic accuracy.
- Existing methods may struggle to effectively remove this artifact.
Purpose of the Study:
- To develop and evaluate a new cascade moving average filter for removing baseline wander from knee joint vibration signals.
- To improve the reliability of diagnostic decision-making in medical studies involving knee vibrations.
Main Methods:
- A novel cascade moving average filter with hierarchical layers was designed.
- The filter utilizes two moving averaging operators in the first layer with overlapping inputs.
- Piecewise linear trends were estimated and smoothed in the final output.
Main Results:
- The proposed cascade filter effectively removed baseline wander from raw knee joint vibration signals.
- Simulation results demonstrated the filter's capability in artifact reduction.
- The method shows potential for enhancing diagnostic accuracy.
Conclusions:
- The developed cascade moving average filter is an effective tool for baseline wander removal in knee joint vibration analysis.
- This technique can improve the quality of data used in computer-aided diagnosis of knee disorders.
- Further clinical validation is recommended to assess its impact on diagnostic decision-making.
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