2020
DOI: 10.3390/s20040970
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A Wavelet Adaptive Cancellation Algorithm Based on Multi-Inertial Sensors for the Reduction of Motion Artifacts in Ambulatory ECGs

Abstract: Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then… Show more

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Cited by 13 publications
(7 citation statements)
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References 16 publications
(15 reference statements)
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“…Denoising techniques must be used for EEG data, with lower voltage values than other bio-electrical signals, or when performing more vigorous activities, to reduce motion artifacts' influence [106,107]. However, we will study its usefulness and feasibility in the future.…”
Section: Artifactsmentioning
confidence: 99%
“…Denoising techniques must be used for EEG data, with lower voltage values than other bio-electrical signals, or when performing more vigorous activities, to reduce motion artifacts' influence [106,107]. However, we will study its usefulness and feasibility in the future.…”
Section: Artifactsmentioning
confidence: 99%
“…However, by using a better reference noise signal that is sensitive to motion, one can design an adaptive filter that can automatically cancel the noise for different ECG frequencies. Many researchers, such as [21,22], use accelerometer data frequently associated with mobile commercial ECG sensors to implement those filters.…”
Section: Adaptive Filtermentioning
confidence: 99%
“…Removing baseline wander and motion artifacts is harder since it is almost impossible to obtain the noise source from body motion. Some researchers [21,22] explore the possibility of using accelerometer data as the reference noise signal. The accelerometer is standard equipment for many commercial mobile healthcare devices.…”
Section: Motion-sensitive Noise Signal Generationmentioning
confidence: 99%
“…Another approach is to denoise the ECG using nonlinear signal processing techniques, e.g., nonlinear Bayesian filtering [ 13 ], adaptive filtering [ 14 , 15 , 16 ], empirical mode decomposition [ 17 ], multiresolution thresholding using the discrete wavelet transform [ 18 , 19 ] or the empirical wavelet transform [ 20 ]. The design of denoising techniques is challenged by the fact that the spectral content of motion artifacts, as well as that of muscle noise, overlaps considerably with that of the QRS complex [ 21 ], thus implying that linear filtering techniques cannot be used.…”
Section: Introductionmentioning
confidence: 99%
“…While denoising may be performed in ambulatory recordings aimed for rhythm analysis, there is a risk that denoising distorts the diagnostic information of ECG recordings aimed for analysis of beat morphology [ 13 ]. So far, performance evaluation of denoising techniques has been expressed in engineering terms, e.g., signal-to-noise ratio [ 14 , 15 , 16 , 20 ], rather than in clinical terms reflecting changes in, e.g., wave amplitude and duration.…”
Section: Introductionmentioning
confidence: 99%