An integration of improved median and morphological filtering techniques for electrocardiogram signal processing

Verma, Rishendra; Mehrotra, Rini; Bhateja, Vikrant

doi:10.1109/iadcc.2013.6514402

Cited by 26 publications

(6 citation statements)

References 17 publications

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“…For better comparison the alpha beta and gamma waves were separated, and their spectrum correlation was calculated. Here spcorr sig shows the spectrum correlation of the original and detrended EEG signals, while spcorr alpha , spcorr beta , spcorr gamma represent the spectrum correlation between the alpha (8-15 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), and gamma (≥ 32 Hz) waves of EEG, respectively, and best fc corresponds to the best cut-off frequency that is used for the filtering of the EEG signal.…”

Section: B Results Of Beads Optimizationmentioning

confidence: 99%

“…Almost all high pass filtering methods have sharp cut-off frequencies, which in many cases distort the raw signal [29]. Using wavelet based techniques for high-frequency noise removal from the physiological signals requires numerous experiments for finding different parameters and thresholds [30]. Other most common methods like Fast Fourier Transform (FFT) [31] or Finite Impulse Response (FIR) filter [32] techniques are limited.…”

Section: Methods and Materials A Overview Of Methodologymentioning

confidence: 99%

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Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

et al. 2019

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We present a method for the removal of movement artifacts from the recordings of electroencephalography (EEG) signals in the context of sports health. We use a smart wearable Internet of Things-based signal recording system to record physiological human signals [EEG, electrocardiography (ECG)] in real time. Then, the movement artifacts are removed using ECG as a reference signal and the baseline estimation and denoising with sparsity (BEADS) filter algorithm for trend removal. The parameters (cut-off frequency) of the BEADS filter are optimized with respect to the number of QRS complexes detected in the reference ECG signal. Next, surrogate movement signals are generated using a linear combination of intrinsic mode functions derived from the sample movement signals by the application of empirical mode decomposition. Surrogate signals are used to test the efficiency of the BEADS method for filtering the movement-contaminated EEG signals. We provide an analysis of the efficiency of the method, extracted movement artifacts and detrended EEG signals. INDEX TERMS Mobile EEG, movement artifact removal, sports e-health, digital signal processing.

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Section: B Results Of Beads Optimizationmentioning

confidence: 99%

Section: Methods and Materials A Overview Of Methodologymentioning

confidence: 99%

Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

et al. 2019

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“…The median filtering algorithm (MF) is a nonlinear filtering method [11] [12], which is widely used in image denoising [13], one-dimensional signal processing [14] and other fields.…”

Section: Noise Reduction Methods Based On Median Filtering For Rdtsmentioning

confidence: 99%

Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

Wang¹,

Liu²,

Wang³

2019

OJAppS

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The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1˚C to 1.2˚C at 40.0˚C, 50.0˚C and 60.0˚C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

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“…The morphological and median filtering techniques have advantages in retaining the details of linear filtering and maintaining computational simplicity. 22 Li and Xiao 23 combined the PCA method and a morphological filter for pattern classification. Although this method provides a solid framework, there are two drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with multilayer signal decomposition, the morphological and median filtering techniques are more suitable for dealing with nonstationary and nonlinear processes. The morphological and median filtering techniques have advantages in retaining the details of linear filtering and maintaining computational simplicity . Li and Xiao combined the PCA method and a morphological filter for pattern classification.…”

Section: Introductionmentioning

confidence: 99%

Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

Fan

Zhu

et al. 2019

Ind. Eng. Chem. Res.

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With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF transformation for output trend features and pattern matching. To accurately evaluate the morphological similarity between the test trends and template trends of ARMF, the dynamic time warping distance is adopted for pattern classification. Finally, the proposed KCVA–ARMF pattern-matching method is developed as an effective fault diagnosis model for complex industrial processes. To validate the performance of the proposed method, case studies using the Tennessee Eastman process are performed. Compared with some other multivariate statistical process monitoring methods, the simulation results indicate that the proposed KCVA–ARMF method can obtain higher accuracy in fault diagnosis, especially for difficult-to-diagnose faults.

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An integration of improved median and morphological filtering techniques for electrocardiogram signal processing

Cited by 26 publications

References 17 publications

Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

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