An emboli detection system based on Dual Tree Complex Wavelet Transform and ensemble learning

Serbes, Görkem; Şakar, Betül Erdoğdu; Gülçür, Halil Özcan; Aydin, Nizamettin

doi:10.1016/j.asoc.2015.08.015

Cited by 20 publications

(2 citation statements)

References 33 publications

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“…The predictions obtained at Level-0 are used as input for Level-1. The model in Level-1 is called a meta-model and learns with the previous level models that give the best prediction of each model of the previous level (Shamaei & Kaedi, 2016;Serbes et al, 2015;Petropoulos et al, 2017). The number of levels is not limited to 2 in the stacking method.…”

Section: Stackingmentioning

confidence: 99%

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Yazici¹,

Boran²

2021

Journal of Intelligent Systems: Theory and Applications

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In wind energy studies, predicting the short-term energy generation amount for wind power plants and determining the production offer to be placed on the market play an important role. In this study an hourly short-term wind power estimation of a wind turbine located in Turkey with an installed power of 3600 kW has been made. Estimation results were evaluated on a seasonal and annual basis. New hybrid models have been developed for short-term wind power prediction, consisting of Bayesian Optimization (BO), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Decision Tree (DT), stacking, and bagging algorithms. In the proposed prediction approach, it is aimed to reduce prediction errors by combining different regression algorithms with the BO method and ensemble algorithms. Unlike other wind prediction studies, BO was used for the first time in the hyperparameter selection of the regression algorithms selected as the basic learner in the study. Bayesian optimized decision tree (BO-DT) with the lowest error values among the base learners, and Bayesian optimized gaussian process regression (BO-GPR) combined with bagging and stacking. The efficiency of ensemble learning algorithms was measured by the statistical measurement methods Normalized Absolute Mean Error (NMAE), Normalized Root of Mean Squares Error (NRMSE), and determination coefficient (R 2 ). According to the results, the bagging method created with the BO-DT took the annual average NRMSE, NMAE, R 2 criteria of 11.045%, 4.880%, 0.899, respectively, and the model with the best performance was selected in terms of both annual and seasonal results.

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Section: Stackingmentioning

confidence: 99%

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Yazici¹,

Boran²

2021

Journal of Intelligent Systems: Theory and Applications

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“…Additionally, in [12], a modified dual tree complex wavelet transform based feature extraction system which is utilized for discriminating artifacts and emboli was given. In another study, dual tree complex wavelet transform is employed in feature extraction and extracted features were given to a model which uses ensemble learning with the aim of emboli, speckle and artifact classification [13]. In all above studies, in order to benefit from the narrow-band frequency characteristics of embolic signals, time-frequency/scale based methods are employed in feature extraction steps.…”

Section: Introductionmentioning

confidence: 99%

A micro emboli vs non-emboli classification system based on the directional dual tree rational dilation wavelet transform

Serbes

Şakar

Aydin

2015

2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)

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Transcranial Doppler (TCD) is a widely used, non-invasive, rapid and reproducible monitoring method for observing the condition of middle cerebral artery. Micro embolic signals, which appear in various clinical scenarios such as; carotid stenosis, aortic arch plaques, atrial fibrillation, myocardial infarction, patent foramen ovale and valvular stenosis, can be detected by the analysis of TCD signals. Discrete wavelet transform based methods were frequently used in literature for micro embolic signal detection. However, in all the previously used complex/non-complex discrete wavelet transform based methods, low Q-factor wavelets were employed for feature extraction. Low Q-factor wavelets have been successfully used for processing piecewise smooth signals but for the embolic signals, a discrete wavelet transform with better frequency resolution is needed. Therefore in this study, a novel Directional Dual Tree Rational Dilation Wavelet Transform (DDT-RADWT), in which the Q-factor of the analysis and synthesis filters can be adjusted due to the properties of signal of interest, is used as the feature extractor. DDT-RADWT is applied to a dataset consisting of 130 micro embolic signals and 130 non-embolic signals (65 artifacts and 65 Doppler speckles) and the obtained coefficients are used as features. In the proposed method, in order to utilize from the different frequency characteristics of micro embolic, artifact and Doppler speckle signals, the DDT-RADWT is applied with high Q-factor filters. The extracted coefficients are given to k-NN and SVM classifiers with the aim of discriminating two classes of micro embolic signals and non-embolic signals. The results show that higher general accuracy and micro embolic signal detection accuracies are obtained with high Q-factor wavelet analysis.

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Moving object detection using statistical background subtraction in wavelet compressed domain

Sengar

Mukhopadhyay

2019

Multimed Tools Appl

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An emboli detection system based on Dual Tree Complex Wavelet Transform and ensemble learning

Cited by 20 publications

References 33 publications

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

A micro emboli vs non-emboli classification system based on the directional dual tree rational dilation wavelet transform

Moving object detection using statistical background subtraction in wavelet compressed domain

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