Abstract
This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.
Highlights
Seismocardiography (SCG) is a non-invasive measurement that records the local vibrations of the chest wall in response to the heartbeat [1]
The data show that the heartbeat signals are contaminated by the artifact, artifact, and the and features and graphs the heartbeat signals cannot identified during the walking motion the features andof graphs of the heartbeat signalsbe cannot be identified during the period
Paper, we we proposed proposed aa novel novel method method based based on on an an adaptive adaptive recursive recursive least least squares squares filter filter to to remove the motion artifact of the acceleration data recorded by only one accelerometer
Summary
Seismocardiography (SCG) is a non-invasive measurement that records the local vibrations of the chest wall in response to the heartbeat [1]. An independent component analysis approach and a normalized least means square (NLMS) adaptive filter to motion artifact cancellation of the SCG signal using two accelerometers were developed in [9] and [10], respectively. An extra moving average method was utilized to obtain the heart rate as the primary heart sound graph was not clear in the extracted motion-free SCG signal. To solve this problem, we present a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the SCG signal that was obtained by one accelerometer.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
