Analysis of Microarray Data by Empirical Wavelet Transform for Cancer Classification Using Block by Block Method

2021 ◽  
Vol 11 (3) ◽  
pp. 697-702
Author(s):  
S. Jayanthi ◽  
C. R. Rene Robin
Keyword(s):  
Wavelet Transform ◽  
Microarray Data ◽  
Hypothesis Test ◽  
Cancer Classification ◽  
Statistical Hypothesis ◽  
Support Vector ◽  
Adaptive Wavelet ◽  
Statistical Hypothesis Test ◽  
Empirical Wavelet Transform ◽  
Powers Of 2

In this study, DNA microarray data is analyzed from a signal processing perspective for cancer classification. An adaptive wavelet transform named Empirical Wavelet Transform (EWT) is analyzed using block-by-block procedure to characterize microarray data. The EWT wavelet basis depends on the input data rather predetermined like in conventional wavelets. Thus, EWT gives more sparse representations than wavelets. The characterization of microarray data is made by block-by-block procedure with predefined block sizes in powers of 2 that starts from 128 to 2048. After characterization, a statistical hypothesis test is employed to select the informative EWT coefficients. Only the selected coefficients are used for Microarray Data Classification (MDC) by the Support Vector Machine (SVM). Computational experiments are employed on five microarray datasets; colon, breast, leukemia, CNS and ovarian to test the developed cancer classification system. The obtained results demonstrate that EWT coefficients with SVM emerged as an effective approach with no misclassification for MDC system.

scholarly journals Image Fusion Based on Statistical Hypothesis Test Using Wavelet Transform

2011 ◽  
Vol 24 (4) ◽  
pp. 695-708 ◽  
Author(s):  
Min-Joon Park ◽  
Min-Jun Kwon ◽  
Gi-Hun Kim ◽  
Han-Seul Shim ◽  
Dong-Hoon Lim
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals

2018 ◽  
Vol 14 (4) ◽  
Author(s):  
Omkar Singh ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Wavelet Transform ◽  
Multivariate Data ◽  
Heterogeneous Data ◽  
Physiological Signals ◽  
Data Series ◽  
Data Set ◽  
Processing Application ◽  
Adaptive Wavelet ◽  
Empirical Wavelet Transform ◽  
Multivariate Signals

Abstract Background This article proposes an extension of empirical wavelet transform (EWT) algorithm for multivariate signals specifically applied to cardiovascular physiological signals. Materials and methods EWT is a newly proposed algorithm for extracting the modes in a signal and is based on the design of an adaptive wavelet filter bank. The proposed algorithm finds an optimum signal in the multivariate data set based on mode estimation strategy and then its corresponding spectra is segmented and utilized for extracting the modes across all the channels of the data set. Results The proposed algorithm is able to find the common oscillatory modes within the multivariate data and can be applied for multichannel heterogeneous data analysis having unequal number of samples in different channels. The proposed algorithm was tested on different synthetic multivariate data and a real physiological trivariate data series of electrocardiogram, respiration, and blood pressure to justify its validation. Conclusions In this article, the EWT is extended for multivariate signals and it was demonstrated that the component-wise processing of multivariate data leads to the alignment of common oscillating modes across the components.

Item-specific overlap between hallucinatory experiences and cognition in the general population: A three-step multivariate analysis of international multi-site data

2021 ◽  
Author(s):  
Abhijit Mahesh Chinchani ◽  
Mahesh Menon ◽  
Meighen Roes ◽  
Heungsun Hwang ◽  
Paul Allen ◽  
...  
Keyword(s):  
Signal Detection ◽  
Source Monitoring ◽  
Hypothesis Test ◽  
The Other ◽  
Statistical Hypothesis ◽  
Auditory Signal ◽  
Attentional Processes ◽  
Hearing Voices ◽  
Statistical Hypothesis Test ◽  
Using Data

Cognitive mechanisms hypothesized to underlie hallucinatory experiences (HEs) include dysfunctional source monitoring, heightened signal detection, or impaired attentional processes. HEs can be very pronounced in psychosis, but similar experiences also occur in nonclinical populations. Using data from an international multisite study on nonclinical subjects (N = 419), we described the overlap between two sets of variables - one measuring cognition and the other HEs - at the level of individual items, allowing extraction of item-specific signal which might considered off-limits when summary scores are analyzed. This involved using a statistical hypothesis test at the multivariate level, and variance constraints, dimension reduction, and split-half reliability checks at the level of individual items. The results showed that (1) modality-general HEs involving sensory distortions (hearing voices/sounds, troubled by voices, everyday things look abnormal, sensations of presence/movement) were associated with more liberal auditory signal detection, and (2) HEs involving experiences of sensory overload and vivid images/imagery (viz., HEs for faces and intense daydreams) were associated with other-ear distraction and reduced laterality in dichotic listening. Based on these results, it is concluded that the overlap between HEs and cognition variables can be conceptualized as modality-general and bi-dimensional: one involving distortions, and the other involving overload or intensity.

scholarly journals Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

Computation ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 59 ◽  
Author(s):  
Giovanni Delnevo ◽  
Silvia Mirri ◽  
Marco Roccetti
Keyword(s):  
Granger Causality ◽  
Air Pollutants ◽  
Climatic Factors ◽  
Virus Disease ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Clear Correlation ◽  
The Real ◽  
Statistical Hypothesis Test ◽  
Before And After

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated.

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