1-D signal denoising using wavelets based optimization of polynomial threshold function

Tulsani, Hemant; Gupta, Rashmi

doi:10.1109/icrito.2014.7014720

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…With the results described in Section 5, in which hundreds of signals (measured and simulated) were evaluated, the ability of the Fleming function and its Fleming 2 variant to overcome the most common functions such as Hard and Soft, as well as twelve other alternatives presented in some publications [15,19,22,[35][36][37][38]. The Fleming function can be applied with different inclination values, but for PD signals, the ideal is that these values are limited between 5 and 10 to provide the best results.…”

Section: Discussionmentioning

confidence: 99%

“…The choice of most of these parameters has already been widely explored in several works meant to PD processing [2,[6][7][8][9][10][11][12][13]. However, with respect to threshold functions, most studies do not focus on PD signals denoising but on audio signals [14][15][16][17][18], Electrocardiogram (ECG) [19][20][21] or images [22][23][24][25][26][27][28]. Therefore, there is a lack of a dedicated threshold function to improve the PD pulses denoising, in order to increase the precision in the diagnosis of High Voltage (HV) equipment.…”

Section: Introductionmentioning

confidence: 99%

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A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Cunha¹,

Petraglia²,

Carvalho³

et al. 2021

Wavelet Theory

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Based on the wavelet transform filtering theory, the chapter will describe the elaboration of a wavelet threshold function intended for the denoising of the partial discharge phenomenon measurements. This new function, conveniently named Fleming threshold, is based on the logistic function, which is well known for its utility in several important areas. In the development is shown some variations in the application of the Fleming function, in an attempt to identify the decomposition levels where the thresholding process must be more stringent and those where it can be more lenient, which increases its effectiveness in the removal of noisy coefficients. The proposed function and its variants demonstrate excellent results compared to other wavelet thresholding methods already described in the literature, including the famous Hard and Soft functions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Cunha¹,

Petraglia²,

Carvalho³

et al. 2021

Wavelet Theory

View full text Add to dashboard Cite

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“…Therefore, it is appropriate to keep balance between the removal of noises and the conservation of signal waves. Widely-used threshold calculation rules primarily include the sqtwolog rule, the minimax rule, the heursure rule, the rigrsure rule, and so forth [7]- [9].…”

Section: Wavelets For Ecg Analysis a Wavelets For Signal Denoisimentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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“…The amount of asymmetry is subject to the disparity among X med (i) -X min (i) and X max (i) -X med (i). Wavelets are capable of functionally confining a signal in both time and frequency space, therefore, enabling the transformed data to be concurrently examined in both domains [25]. The wavelet transform of the signal plus noise produces the wavelet coefficients that indicate the correlation coefficients among the noise-corrupted EEG and the wavelet function.…”

Section: Preprocessingmentioning

confidence: 99%

“…This tool is also used to know the cause of unconsciousness in comatose patients [3]. Spectral information of EEG signal can be obtained by focusing on the frequency bands, namely, Alpha waves (8)(9)(10)(11)(12), Beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), Gamma waves (above 30 Hz), Theta waves (4)(5)(6)(7)(8) and Delta waves (1)(2)(3)(4). These enable easy understanding for an accurate diagnosis of the classifications as mentioned above, signifing a different mental state of a patient.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

Anand

Jaiswal

Ghosh

2018

CSENG

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Background: The rapid improvement in technology enables an Electroencephalogram (EEG) to detect a diverse range of brain disorders easily. The design of sophisticated signal processing methods for an efficient analysis of the EEG signals is exceptionally essential. Raw EEG signal is contaminated by noise and artefacts that modify the spectral-spatial and temporal information of the signal and renders inaccurate clinical interpretation. Denoising of the signal is the first step to refine the signal quality and identify patient's mental state from the signal although it is not an easy task because of high dimensionality and complexity of EEG signal. The present study highlights three conditions of the brain namely stroke, brain death, and a healthy state. The primary concern is to detect the most abnormal conditions of the brain, i.e., an EEG with a critical stage.Method: This paper introduces a neoteric technique for the analysis of EEG signals of the three conditions using filters such as Fuzzy filter and wavelet orthogonal filter to obtain highly accurate resultant signals. Further, the resultant filter is trained in Neural Network for predicting the brain abnormalities. The proposed system is found to be efficient in denoising the EEG waves. Results:The result shows that the classification accuracy of multiclass EEG dataset achieved and the performance of ANN is high and it was found to be the best validation performance of ANN which is 0.2303. Conclusion:This paper comprehensively describes the denoising of the EEG signals that will provide accuracy in the diagnosis of the EEG to detect brain disorders. The Fuzzy filter pre-processes the signals by considering the noisy signal by an ideal value in such a way that the desired metric (the filtered output) is reduced. The orthogonal wavelet filter produces a single scaling function and wavelet function. The EEG features are extracted from multiple-level decompositions of EEGs by DWT. Finally, the features are classified using Back propagation artificial neural network that categorizes the EEGs to make the diagnosis easier for the brain abnormalities.

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1-D signal denoising using wavelets based optimization of polynomial threshold function

Cited by 5 publications

References 18 publications

A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Wavelets for Electrocardiogram: Overview and Taxonomy

A Machine Learning Prediction Model for Automated Brain Abnormalities Detection

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