Motion Artifact Cancellation of Seismocardiographic Recording From Moving Subjects

This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to...

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Published inIEEE sensors journal Vol. 16; no. 14; pp. 5702 - 5708
Main Authors Yang, Chenxi, Tavassolian, Negar
Format Journal Article
LanguageEnglish
Published New York IEEE 15.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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ISSN1530-437X
1558-1748
DOI10.1109/JSEN.2016.2573269

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Abstract This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor node. Standardized experiments were performed on 40 moving adult subjects. False-positives were ruled out for a more precise detection rate. The research on sliding ensemble average was also conducted to find the minimum required window size. The results indicate a detection rate of 96% and a sliding window size of 32 intervals for robust continuous monitoring, showing that adaptive filtering could be a promising technique for the cancellation of motion noise artifacts from SCG recordings in moving subjects.
AbstractList This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor node. Standardized experiments were performed on 40 moving adult subjects. False-positives were ruled out for a more precise detection rate. The research on sliding ensemble average was also conducted to find the minimum required window size. The results indicate a detection rate of 96% and a sliding window size of 32 intervals for robust continuous monitoring, showing that adaptive filtering could be a promising technique for the cancellation of motion noise artifacts from SCG recordings in moving subjects.
Author Tavassolian, Negar
Yang, Chenxi
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Keywords wearable sensor network
Adaptive filtering
MEMS accelerometer
seismocardiography (SCG)
LMS filtering
motion artifact
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SubjectTerms Accelerometers
Adaptive filtering
Adaptive filters
Adults
Cancellation
Electrocardiography
LMS filtering
Matlab
MEMS accelerometer
Micromechanical devices
Motion artifact
Motion artifacts
Recording
Seismocardiography (SCG)
Sensors
Sliding
Wearable sensor network
Title Motion Artifact Cancellation of Seismocardiographic Recording From Moving Subjects
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