The Experts below are selected from a list of 75921 Experts worldwide ranked by ideXlab platform
Yugang Jiang - One of the best experts on this subject based on the ideXlab platform.
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stellar data classification using svm with wavelet transformation
Systems Man and Cybernetics, 2004Co-Authors: Ping Guo, Fei Xing, Yugang JiangAbstract:This paper presents a novel stellar spectra recognition Technique, which is based on a wavelet transform and support vector machines. Due to the very low signal-to-noise ratio of real world spectral data, a de-noising method for stellar spectra is proposed using a wavelet transform based on the traditional Threshold Technique. Then support vector machines are adopted to complete the classification. Features in the spatial and wavelet domain are extracted and then used as input of support vector machines. Experimental results show that our Technique is robust against noise and efficient in computation. The obtained correct classification rate of the proposed methods is much higher than using either a support vector machine alone or the principle component analysis feature extraction method.
Brett Hauber - One of the best experts on this subject based on the ideXlab platform.
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parkinson s patients tolerance for risk and willingness to wait for potential benefits of novel neurostimulation devices a patient centered Threshold Technique study
MDM Policy & Practice, 2021Co-Authors: Brett Hauber, Brennan Mange, Mo Zhou, Shomesh Chaudhuri, Heather L Benz, Brittany Caldwell, John P Ruiz, Anindita Saha, Stephanie Christopher, Dawn BardotAbstract:Background. Parkinson's disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients' preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design. Methods. We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold Technique was used to quantify patients' risk Thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in "on-time," motor symptoms, pain, cognition, and pill burden. The survey elicited patients' willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study. Results. A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk Thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than Threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit. Conclusions. PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.
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using the Threshold Technique to elicit patient preferences an introduction to the method and an overview of existing empirical applications
Applied Health Economics and Health Policy, 2020Co-Authors: Brett Hauber, Joshua R CoulterAbstract:Patient preference information (PPI) is a topic of interest to regulators and industry. One of many known methods for eliciting PPI is the Threshold Technique (TT). However, empirical studies of the TT differ from each other in many ways and no effort to date has been made to summarize them or the evidence regarding the performance of the method. We sought to describe the TT and summarize the empirical applications of the method. Forty-three studies were reviewed. Most studies estimated the minimum level of benefit required to make a treatment worthwhile, and over half estimated the maximum level of risk patients would accept to achieve a treatment benefit. The evidence demonstrates that the TT can be used to elicit multiple types of Thresholds and can be used to explore preference heterogeneity and preference non-linearity. Some evidence suggests that the method may be sensitive to anchoring and shift-framing effects; however, no evidence suggests that the method is more or less sensitive to these potential biases than other stated-preference methods. The TT may be a viable method for eliciting PPI to support regulatory decision-making; however, additional understanding of the performance of this method may be needed. Future research should focus on TT performance compared with other stated-preference methods, the extent to which results predict patient choice, and the ability of the TT to inform individual treatment decisions at the point of healthcare delivery.
Keigo Endo - One of the best experts on this subject based on the ideXlab platform.
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semi automated renal region of interest selection method using the double Threshold Technique inter operator variability in quantitating 99mtc mag3 renal uptake
European Journal of Nuclear Medicine and Molecular Imaging, 1997Co-Authors: Yumi Tomaru, Tomio Inoue, Noboru Oriuchi, Kazukuni Takahashi, Keigo EndoAbstract:In calculating the relative and absolute renal uptake of technetium-99m mercaptoacetyltriglycine (MAG3), inter-operator variability in the assignment of the renal region of interest (ROI) is a critical factor. Our goal was to develop a semi-automated method of assigning the renal ROI and then to compare the inter-operator variability in calculating the percent injected dose (%ID) in the kidney at 1–2 min, using semi-automated versus manual ROIs. The manual ROIs were drawn independently by three operators (A, B and C). Operator A had about 20 years, experience in nuclear medicine, while operators B and C respectively had 3 years and 1 year of experience. In the semi-automated renal ROI selection method using the double-Threshold Technique, the operators only click around the centre of each kidney. The same three operators processed the ROIs using this double-Threshold method on 1–2 min images. The semi-automated method failed in three kidneys with very markedly reduced function owing to superimposition by liver or spleen. Inter-operator reproducibility in the remaining 59 kidneys was estimated using manual and semi-automated ROIs. With manual ROIs, the %ID (mean±standard error of mean) was 4.32±0.167 for A, 4.14±0.165 for B and 3.28±0.139 for C. Although there was good correlation among them, these values were significantly different (P<0.0001). Using semi-automated ROIs, the %ID was 4.38±0.160 for three operators. No significant difference was observed. Complete reproducibility was shown in 58 of 59 kidneys; the %ID difference of the remaining kidney was only 1.2%. The lowest %ID of all the kidneys successfully detected using the semi-automated method was 0.77%. The semi-automated renal ROI selection method using the double-Threshold Technique displays good detectability of the renal contour. The renal uptake calculated using this method is reproducible and acceptable in routine clinical practice.
Tao Jian - One of the best experts on this subject based on the ideXlab platform.
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Study on the Robust Wavelet Threshold Technique for Heavy-tailed Noises
Journal of Computers, 2011Co-Authors: Guang-fen Wei, Tao JianAbstract:Interesting signals are often contaminated by heavy-tailed noise that has more outliers than Gaussian noise. Under the introduction of probability model for heavy-tailed noises, a robust wavelet Threshold based on the minimax description length principle is derived in the e-contaminated normal family for maximizing the entropy. The performance and their measurement criterion for the robust wavelet Threshold are studied in this paper. By the proposed performance measurement criterion, several kinds of noisy signals are processed with the wavelet Thresholding Techniques. Compared with classical Threshold based on Gaussian assumption, the robust Threshold can eliminate the heavy-tailed noise better, even if the precise value of e is unknown, which shows its robustness. The further experiment shows that soft Threshold is more suitable than hard Threshold for robust wavelet Threshold Technique. Finally, the robust Threshold Technique is applied to denoise the practically measured gas sensor dynamic signals. Results show its good performances.
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Performance Analysis of a Robust Wavelet Threshold for Heavy-Tailed Noises
Applied Mechanics and Materials, 2010Co-Authors: Guang-fen Wei, Tao JianAbstract:The interesting signal is often contaminated by heavy-tailed noise that has more outliers than Gaussian noise. A robust wavelet Threshold based on the minimax description length principle is derived in the e-contaminated normal family for maximizing the entropy. Compared with classical Threshold based on Gaussian assumption, the robust Threshold can eliminate the heavy-tailed noise better, even if the precise value of e is unknown, which shows its robustness. The further experiment shows that soft Threshold is more suitable than hard Threshold for robust wavelet Threshold Technique.
Ping Guo - One of the best experts on this subject based on the ideXlab platform.
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stellar data classification using svm with wavelet transformation
Systems Man and Cybernetics, 2004Co-Authors: Ping Guo, Fei Xing, Yugang JiangAbstract:This paper presents a novel stellar spectra recognition Technique, which is based on a wavelet transform and support vector machines. Due to the very low signal-to-noise ratio of real world spectral data, a de-noising method for stellar spectra is proposed using a wavelet transform based on the traditional Threshold Technique. Then support vector machines are adopted to complete the classification. Features in the spatial and wavelet domain are extracted and then used as input of support vector machines. Experimental results show that our Technique is robust against noise and efficient in computation. The obtained correct classification rate of the proposed methods is much higher than using either a support vector machine alone or the principle component analysis feature extraction method.
