Reference signal less Fourier analysis based motion artifact removal algorithm for wearable photoplethysmography devices to estimate heart rate during physical exercises

2021 ◽  
pp. 105081
Author(s):  
Pankaj ◽  
Ashish Kumar ◽  
Rama Komaragiri ◽  
Manjeet Kumar
Keyword(s):  
Heart Rate ◽  
Fourier Analysis ◽  
Reference Signal ◽  
Motion Artifact ◽  
Artifact Removal ◽  
Physical Exercises

Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis

2019 ◽  
Vol 19 (24) ◽  
pp. 12432-12442 ◽  
Author(s):  
Deepak Berwal ◽  
Vandana C.R. ◽  
Sourya Dewan ◽  
Jiji C.V. ◽  
Maryam Shojaei Baghini
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Motion Artifact ◽  
Heart Rate Variability Analysis ◽  
Ecg Signal ◽  
Artifact Removal ◽  
Variability Analysis ◽  
Ambulatory Ecg

Application of Biomedical Signal Acquisition Equipment in Human Sport Heart Rate Monitoring

2020 ◽  
Vol 10 (4) ◽  
pp. 877-883
Author(s):  
Le He
Keyword(s):  
Heart Rate ◽  
Motion Artifact ◽  
Human Motion ◽  
Signal Quality ◽  
Signal Acquisition ◽  
Heart Rate Monitoring ◽  
Artifact Removal ◽  
Biomedical Signal ◽  
Baseline Drift ◽  
Sensor Signals

Aiming at exploring biomedical signal acquisition equipment used in human motion heart rate monitoring, the research on the related hardware design and signal processing method was carried out. A biomedical signal acquisition device based on photoplethysmography (PPG) is designed, and the equipment was applied to acquire PPG signals and acceleration sensor signals under different motion states. The analysis of the experimental data showed that, the fusion method of the acceleration sensing information in the motion artifact removal method is perfected. The effectiveness of the baseline drift removal algorithm, motion artifact removal algorithm and dynamic heart rate monitoring algorithm was verified by reconstructing the signal quality evaluation index. To sum up, taking MINDRAY VS-800 as a reference device, it is compared with the adaptive filtering technology in terms of signal quality, BPM detection results and algorithm complexity, and better results are finally obtained.

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 Use of Transforms in Biomedical Signal Processing and Analysis

2021 ◽  
Author(s):  
Ette Harikrishna ◽  
Komalla Ashoka Reddy
Keyword(s):  
Wavelet Transforms ◽  
Research Work ◽  
Reference Signal ◽  
Low Frequency ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Biomedical Signal Processing ◽  
Blood Oxygen Saturation ◽  
Biomedical Signals ◽  
Electrocardiogram Ecg

Biomedical signals like electrocardiogram (ECG), photoplethysmographic (PPG) and blood pressure were very low frequency signals and need to be processed for further diagnosis and clinical monitoring. Transforms like Fourier transform (FT) and Wavelet transform (WT) were extensively used in literature for processing and analysis. In my research work, Fourier and wavelet transforms were utilized to reduce motion artifacts from PPG signals so as to produce correct blood oxygen saturation (SpO2) values. In an important contribution we utilized FT for generation of reference signal for adaptive filter based motion artifact reduction eliminating additional sensor for acquisition of reference signal. Similarly we utilized the transforms for other biomedical signals.

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