Wavelet-based noise removal for biomechanical signals: a comparative study

Wachowiak, Mark P.; Rash, Gregory S.; Quesada, Peter M.; Desoky, A.

doi:10.1109/10.827298

Cited by 62 publications

(24 citation statements)

References 14 publications

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“…Considerable interest has arisen in recent years regarding wavelets as a new transformation technique for kinematical signal processing [13,14]. Wavelet transform enables the analysis of data at multiple levels of resolution (scale).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

et al. 2004

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

et al. 2004

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“…However, some intrinsic shortcomings of these methods hinder them from being widely applied in real-world applications. Take the popular signal-based denoising methods (e.g., Gaussian low-pass filter and discrete cosine transform (DCT)) for example, although they are easy to implement and only require a little of computational cost, they ignore the underlying structure correlation between different human joints and cannot preserve the embedded spatial-temporal motion patterns [11][12][13][14][15]. Indeed, human motion involves highly coordinated movement and the movement between different human joints are not independently [8].…”

Section: Introductionmentioning

confidence: 99%

Sparse motion bases selection for human motion denoising

Xiao

Feng

et al. 2015

Signal Processing

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a b s t r a c tHuman motion denoising is an indispensable step of data preprocessing for many motion data based applications. In this paper, we propose a data-driven based human motion denoising method that sparsely selects the most correlated subset of motion bases for clean motion reconstruction. Meanwhile, it takes the statistic property of two common noises, i.e., Gaussian noise and outliers, into account in deriving the objective functions. In particular, our method firstly divides each human pose into five partitions termed as poselets to gain a much fine-grained pose representation. Then, these poselets are reorganized into multiple overlapped poselet groups using a lagged window moving across the entire motion sequence to preserve the embedded spatial-temporal motion patterns. Afterward, five compacted and representative motion dictionaries are constructed in parallel by means of fast K-SVD in the training phase; they are used to remove the noise and outliers from noisy motion sequences in the testing phase by solving ℓ 1 -minimization problems. Extensive experiments show that our method outperforms its competitors. More importantly, compared with other data-driven based method, our method does not need to specifically choose the training data, it can be more easily applied to real-world applications.

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“…These WFs are chosen based on the shapes of the mother wavelet, which are similar to that of EEG signal [7,9]. RMS difference was calculated to measure the effectiveness of the noise removal using these wavelets.…”

Section: Introductionmentioning

confidence: 99%

A Study on Discrete Wavelet-Based Noise Removal from EEG Signals

Asaduzzaman

Reaz²,

Mohd-Yasin³

et al. 2010

Advances in Experimental Medicine and Biology

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Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.

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Wavelet-based noise removal for biomechanical signals: a comparative study

Cited by 62 publications

References 14 publications

Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

Ambulatory system for the quantitative and qualitative analysis of gait and posture in chronic pain patients treated with spinal cord stimulation

Sparse motion bases selection for human motion denoising

A Study on Discrete Wavelet-Based Noise Removal from EEG Signals

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