scholarly journals Soft IP Core Implementation of Recursive Least Squares Filter using Only Multplicative and Additive Operators

2007 ◽  
Author(s):  
Gaye Lightbody ◽  
Roger Woods ◽  
Jonathan Francey
Keyword(s):  
Least Squares ◽  
Recursive Least Squares ◽  
Ip Core ◽  
Recursive Least Squares Filter

scholarly journals A Novel Adaptive Recursive Least Squares Filter to Remove the Motion Artifact in Seismocardiography

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1596 ◽  
Author(s):  
Shuai Yu ◽  
Sheng Liu
Keyword(s):  
Heart Rate ◽  
Least Squares ◽  
Motion Artifact ◽  
Recursive Least Squares ◽  
Detection Accuracy ◽  
Artifact Removal ◽  
Ecg Signals ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Recursive Least Squares Filter

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.

scholarly journals Adaptive Noise Cancellation System Using a Recursive Least Squares Filter

Respuestas ◽  
2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yesica Beltrán-Gómez ◽  
Jorge Gómez-Rojas ◽  
Rafael Linero-Ramos
Keyword(s):  
Least Squares ◽  
Initial Conditions ◽  
Low Cost ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Adaptive Noise Cancellation ◽  
Forgetting Factor ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Recursive Least Squares Filter

In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6  is reached, and with this it makes possible a low-cost implementation.

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