Wavelet basis functions in biomedical signal processing

Rafiee, Javad; Rafiee, Mohammad A.; Prause, Nicole; Schoen, Marco P.

doi:10.1016/j.eswa.2010.11.050

Cited by 162 publications

(96 citation statements)

References 29 publications

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“…This study is used wavelet transform because of simple and naive for denoise represented of single level discrete 1-D wavelet transform that is the state-of-the-art wavelet denoising [9]. This method has become common signal processing technique in different area and the result indicate that wavelet functions Daubechies 44 (db44) provides a better fit to tested biosignals [22] but experiment of this study is found that the Coiflets1 wavelet is better suited in this study. In addition, the wavelet transform has powerful tool because this process is removed the noise of signal and image [14].…”

Section: The Wavelet Shrinkage Denoising Methodsmentioning

confidence: 73%

A New Wavelet Denoising Method for Noise Threshold

Jangjit¹,

Ketcham²

2017

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Abstract. This study is proposed a new adaptive threshold based on noisy speech for each sub-bands with low complex and it is suitability for range of human hearing and range of hearing test. A new method is used wavelet 1-D experimental signal for denoising. It provided the optimal adaptive threshold of three sub-band with applies to the detail coefficients. The speech enhancement is used of threshoding on the adpated wavelet coefficients, and the results are compared a variety of noisy speech and four well-known benchmark signals. The results, measured objectively by Signal-to-Noise ratio (SNR) and Mean Square Error (MSE), are given for additive white Gaussian noise as well as two different types of noisy environment. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114 % and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods.

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Section: The Wavelet Shrinkage Denoising Methodsmentioning

confidence: 73%

A New Wavelet Denoising Method for Noise Threshold

Jangjit¹,

Ketcham²

2017

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“…We have used a generalized algorithm for the selection of a wavelet basis function and scale which is illustrated by Rafiee et al (2011). In this method, a wavelet basis function is chosen such that maximum wavelet coefficients are obtained when there is maximum resemblance between the wavelet and the signal in terms of its spectral features.…”

Section: Selection Of Wavelet and Scale For N170 Erpmentioning

confidence: 99%

“…This is also evident from their morphological similarity shown here. Finally, the scale at which the value of the sum S n (a) is maximum is chosen as the best match (Rafiee et al, 2011) for the specific channel n. Due to the characteristic nature of the averaged ERP, we observed that the values for best scale varied across the channels. On average it was noted that a scale of 65 was the best scale returned for the different channels.…”

Section: Selection Of Wavelet and Scale For N170 Erpmentioning

confidence: 99%

A wavelet based algorithm for the identification of oscillatory event-related potential components

Aniyan¹,

Philip²,

Samar

et al. 2014

Journal of Neuroscience Methods

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Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

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“…(15,18), as wavelets ortogonais criadas por Ingrid Daubechies no início dos anos 80 foram as primeiras a tornar prática a realização da análise discreta. Construídas com base no projeto de filtros ortogonais, com máximo de relação na resposta em frequência em 0 e metade da taxa de amostragem, impondo como restrição o número de momentos de decaimento (que definem a banda de transição) em uma determinada faixa, proporcionaram uma característica conhecida como maximally flat, obtendo-se assim uma melhor resolução no domínio tempo-frequência.…”

Section: Visando O Diagnóstico De Doenças Da Laringe Os Médicos Usuaunclassified

“…Sinais biológicos normalmente armazenam pouca energia nas faixas de altas frequências, sendo melhor analisados pelos filtros de Daubechies (15,18). No trabalho de Souza (21), o sistema foi projetado com base em uma estrutura composta por duas SVMs (Support Vector Machine) com kernels Gaussianos, associadas com uma rotina de decisão.…”

Section: Visando O Diagnóstico De Doenças Da Laringe Os Médicos Usuaunclassified

Uma nova família de filtros digitais para classificação de dados com aplicações ao pré-diagnóstico de patologias na laringe

Rodrigues¹

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À minha família, com amor, admiração e gratidão por sua compreensão, carinho, presença e incansável apoio ao longo do período de elaboração deste trabalho. AGRADECIMENTOSAo professor, orientador e amigo Rodrigo Capobianco Guido, que, nos anos de convivência, muito me ensinou, contribuindo para meu crescimento científico, intelectual e profissional.Agradeço por toda paciência nos momentos difíceis e pela grande quantidade de horas dispendida em suas reuniões para orientações. The main purpose of this thesis is the development of a new family of digital filters used for data classification, particularly applied to the pre-diagnosis of pathologies in the larynx. A brief bibliographical review, that concentrates on the functioning of the human vocal tract, on the process of disease diagnosis, and on the discrete wavelet transform, which formed the basis for the construction of the proposed filters, is presented. The technology used to develop these new families of filters, that is based on the Daubechies' Wavelet Transform, is also described, moreover, a comparison with other techniques described in the specialized literature for the same purpose is also presented. The investigation shows the results obtained with the proposed technique, in which the accuracy of 100% in normal voice classifications and of 95,52% in pathological voice classifications, was obtained.

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Wavelet basis functions in biomedical signal processing

Cited by 162 publications

References 29 publications

A New Wavelet Denoising Method for Noise Threshold

A New Wavelet Denoising Method for Noise Threshold

A wavelet based algorithm for the identification of oscillatory event-related potential components

Uma nova família de filtros digitais para classificação de dados com aplicações ao pré-diagnóstico de patologias na laringe

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