scholarly journals Spectrotemporal Modulation Sensitivity as a Predictor of Speech Intelligibility for Hearing-Impaired Listeners

2013 ◽  
Vol 24 (04) ◽  
pp. 293-306 ◽  
Author(s):  
Joshua G.W. Bernstein ◽  
Golbarg Mehraei ◽  
Shihab Shamma ◽  
Frederick J. Gallun ◽  
Sarah M. Theodoroff ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Speech Intelligibility ◽  
Stepwise Regression ◽  
Hearing Aid ◽  
Frequency Selectivity ◽  
Hearing Impaired ◽  
Stepwise Regression Analysis ◽  
Detection Thresholds ◽  
Processing Ability ◽  
Speech Intelligibility Index

Background: A model that can accurately predict speech intelligibility for a given hearing-impaired (HI) listener would be an important tool for hearing-aid fitting or hearing-aid algorithm development. Existing speech-intelligibility models do not incorporate variability in suprathreshold deficits that are not well predicted by classical audiometric measures. One possible approach to the incorporation of such deficits is to base intelligibility predictions on sensitivity to simultaneously spectrally and temporally modulated signals. Purpose: The likelihood of success of this approach was evaluated by comparing estimates of spectrotemporal modulation (STM) sensitivity to speech intelligibility and to psychoacoustic estimates of frequency selectivity and temporal fine-structure (TFS) sensitivity across a group of HI listeners. Research Design: The minimum modulation depth required to detect STM applied to an 86 dB SPL four-octave noise carrier was measured for combinations of temporal modulation rate (4, 12, or 32 Hz) and spectral modulation density (0.5, 1, 2, or 4 cycles/octave). STM sensitivity estimates for individual HI listeners were compared to estimates of frequency selectivity (measured using the notched-noise method at 500, 1000, 2000, and 4000 Hz), TFS processing ability (2 Hz frequency-modulation detection thresholds for 500, 1000, 2000, and 4000 Hz carriers) and sentence intelligibility in noise (at a 0 dB signal-to-noise ratio) that were measured for the same listeners in a separate study. Study Sample: Eight normal-hearing (NH) listeners and 12 listeners with a diagnosis of bilateral sensorineural hearing loss participated. Data Collection and Analysis: STM sensitivity was compared between NH and HI listener groups using a repeated-measures analysis of variance. A stepwise regression analysis compared STM sensitivity for individual HI listeners to audiometric thresholds, age, and measures of frequency selectivity and TFS processing ability. A second stepwise regression analysis compared speech intelligibility to STM sensitivity and the audiogram-based Speech Intelligibility Index. Results: STM detection thresholds were elevated for the HI listeners, but only for low rates and high densities. STM sensitivity for individual HI listeners was well predicted by a combination of estimates of frequency selectivity at 4000 Hz and TFS sensitivity at 500 Hz but was unrelated to audiometric thresholds. STM sensitivity accounted for an additional 40% of the variance in speech intelligibility beyond the 40% accounted for by the audibility-based Speech Intelligibility Index. Conclusions: Impaired STM sensitivity likely results from a combination of a reduced ability to resolve spectral peaks and a reduced ability to use TFS information to follow spectral-peak movements. Combining STM sensitivity estimates with audiometric threshold measures for individual HI listeners provided a more accurate prediction of speech intelligibility than audiometric measures alone. These results suggest a significant likelihood of success for an STM-based model of speech intelligibility for HI listeners.

scholarly journals Determinants of In-Hospital Mortality in Elderly Patients Aged 80 Years or above with Acute Heart Failure: A Retrospective Cohort Study at a Single Rural Hospital

2021 ◽  
Vol 10 (7) ◽  
pp. 1468
Author(s):  
Yusuke Watanabe ◽  
Kazuko Tajiri ◽  
Hiroyuki Nagata ◽  
Masayuki Kojima
Keyword(s):  
Heart Failure ◽  
Regression Analysis ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Acute Heart Failure ◽  
Stepwise Regression ◽  
Stepwise Regression Analysis ◽  
Antibiotic Administration ◽  
Very Elderly ◽  
Survivor Group

Heart failure is one of the leading causes of mortality worldwide. Several predictive risk scores and factors associated with in-hospital mortality have been reported for acute heart failure. However, only a few studies have examined the predictors in elderly patients. This study investigated determinants of in-hospital mortality in elderly patients with acute heart failure, aged 80 years or above, by evaluating the serum sodium, blood urea nitrogen, age and serum albumin, systolic blood pressure and natriuretic peptide levels (SOB-ASAP) score. We reviewed the medical records of 106 consecutive patients retrospectively and classified them into the survivor group (n = 83) and the non-survivor group (n = 23) based on the in-hospital mortality. Patient characteristics at admission and during hospitalization were compared between the two groups. Multivariate stepwise regression analysis was used to evaluate the in-hospital mortality. The SOB-ASAP score was significantly better in the survivor group than in the non-survivor group. Multivariate stepwise regression analysis revealed that a poor SOB-ASAP score, oral phosphodiesterase 3 inhibitor use, and requirement of early intravenous antibiotic administration were associated with in-hospital mortality in very elderly patients with acute heart failure. Severe clinical status might predict outcomes in very elderly patients.

scholarly journals Determination of individual teleroentgenographic characteristics of the face profile in ukrainian young men and girls with orthognathic bite

2018 ◽  
pp. 28-34
Author(s):  
М. О. Dmitriev
Keyword(s):  
Regression Analysis ◽  
Soft Tissue ◽  
Young Women ◽  
Stepwise Regression ◽  
Young Men ◽  
Stepwise Regression Analysis ◽  
Regression Equations ◽  
Determination Coefficient ◽  
Normal Occlusion

Modern dentistry requires the definition of individualized values of teleroentgenographic indicators. To solve such problems, methods of regression and correlation analysis are increasingly used, which help to establish not only the existence of various relationships between the anatomical structures of the head and the parameters of the dento-jaw system, but also allow more accurately predict the change in the contour of soft facial tissue in response to orthodontic treatment. The purpose of the study is to develop mathematical models for the determination of individual teleroentgenographic characteristics of the facial soft tissues by studying the cephalometric indices of young men and women of Ukraine with normal occlusion and balanced faces and conducting a direct stepwise regression analysis. With the use of Veraviewepocs 3D device, Morita (Japan) from 38 young men (17 to 21 years of age) and 55 young women (aged from 16 to 20 years) with occlusal close to the orthognathic bite and balanced faces received side teleroentgenograms. The cephalometric analysis was performed using OnyxCeph³™ licensed software. Cephalometric points and measurements were made according to the recommendations of Downs W. B., Holdway R. A., McNamara J., Schwarz A. M., Schmuth G. P. F., Steiner C. C. and Tweed C. H. With the help of direct stepwise regression analysis, in the licensed package “Statistica 6.0”, regression models of individual teleroentgenographic characteristics of the profile of soft facial tissues were constructed. In young men with normal occlusion close to the orthognathic bite of 19 possible models, 11 were constructed with a determination coefficient from 0.638 to 0.930, and in young women – 12 models with a determination coefficient from 0.541 to 0.927. The conducted analysis of models showed that in young men most often the regression equations included – angle N_POG, parameters of which indicate a linear interjaw relation in the anterior-posterior direction (14.0%); angle GL_SNPOG, or index of convexity of the soft tissue profile (8.8%); MAX maxillary length (7.0%), and GL_SN_S index, which defines vertical correlations in the facial profile (5.3%). The young women most often models included – the angle N_POG (12.5%); angle GL_SNPOG (7.5%); soft tissue front angle P_OR_N (6.25%); the reference angle ML_NL and the profile angle T (by 5.0%); the angle AB_NPOG, the angle NBA_PTGN, which defines the direction of development of the mandible and the distance PN_A (3.75%). Thus, in the work with the help of the method of stepwise regression with inclusion, among Ukrainians of adolescence age, based on the characteristics of teleroentgenographic indicators, reliable models of individual teleroentgenographic characteristics of the profile of soft facial tissues were developed and analyzed.

Research on Related Problems of PM2.5 in Air

2014 ◽  
Vol 644-650 ◽  
pp. 5319-5324
Author(s):  
Tian Jiu Leng
Keyword(s):  
Regression Analysis ◽  
Boundary Conditions ◽  
Regression Model ◽  
Multiple Regression ◽  
Stepwise Regression ◽  
Multiple Regression Model ◽  
Stepwise Regression Analysis ◽  
Spatial Evolution ◽  
Analysis Method ◽  
Relevant Factors

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.

Stepwise regression analysis to study male and female factors impacting on pregnancy rate in an intrauterine insemination programme

Andrologia ◽  
2001 ◽  
Vol 33 (3) ◽  
pp. 135-141 ◽  
Author(s):  
M. Montanaro Gauci ◽  
T. F. Kruger ◽  
K. Coetzee ◽  
K. Smith ◽  
J. P. Van Der Merwe ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pregnancy Rate ◽  
Stepwise Regression ◽  
Intrauterine Insemination ◽  
Stepwise Regression Analysis ◽  
Male And Female

Evaluation of an Articulation-Index Based Model for Predicting the Effects of Adaptive Frequency Response Hearing Aids

1990 ◽  
Vol 33 (4) ◽  
pp. 676-689 ◽  
Author(s):  
David A. Fabry ◽  
Dianne J. Van Tasell
Keyword(s):  
Transfer Function ◽  
Frequency Response ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Hearing Impaired ◽  
Articulation Index ◽  
Intelligibility Ratings ◽  
High Pass

The Articulation Index (AI) was used to evaluate an “adaptive frequency response” (AFR) hearing aid with amplification characteristics that automatically change to become more high-pass with increasing levels of background noise. Speech intelligibility ratings of connected discourse by normal-hearing subjects were predicted well by an empirically derived AI transfer function. That transfer function was used to predict aided speech intelligibility ratings by 12 hearing-impaired subjects wearing a master hearing aid with the Argosy Manhattan Circuit enabled (AFR-on) or disabled (AFR-off). For all subjects, the AI predicted no improvements in speech intelligibility for the AFR-on versus AFR-off condition, and no significant improvements in rated intelligibility were observed. The ability of the AI to predict aided speech intelligibility varied across subjects. However, ratings from every hearing-impaired subject were related monotonically to AI. Therefore, AI calculations may be used to predict relative—but not absolute—levels of speech intelligibility produced under different amplification conditions.

Ratings of Rice Leaf Blast Disease Based on Image Processing and Stepwise Regression

2019 ◽  
Vol 35 (6) ◽  
pp. 1037-1043
Author(s):  
Maohua Xiao ◽  
Ziang Deng ◽  
You Ma ◽  
Shishuang Hou ◽  
sanqin Zhao
Keyword(s):  
Image Processing ◽  
Regression Analysis ◽  
Rice Blast ◽  
Stepwise Regression ◽  
Objective Assessment ◽  
Texture Features ◽  
Stepwise Regression Analysis ◽  
Blast Disease ◽  
Leaf Blast ◽  
Variance Method

Abstract. Multi-feature fusion of morphology and texture featuresStepwise regression analysis to distinguish disease areas from natural brown areasCalculate the ratio of the total area of the diseased area to the area of the leaf area to obtain the disease level Abstract. In this research, an evaluation method involving digital image processing and stepwise regression was studied to establish an efficient and accurate rating system for studying rice blast disease. For this purpose, the R-G image was segmented by using maximum interclass variance method in which the lesion and naturally withered region was extracted from the leaves. Then, 240 lesion areas and 240 natural yellow areas were selected as samples. During the experiment, ten morphological features and five texture features were extracted. Subsequently, for lesion identification, stepwise regression analysis, SVM and BP neural network were used. In the results, regression analysis of naturally yellow areas showed the highest accuracy in lesion identification, reaching 93.33% for disaster-level assessment of identified lesion areas. On the basis of the results, it is evident that 153 samples were correctly classified into divisions of 160 tested different rice blast leaves, with 95.63% classification accuracy. This study has introduced a new method for objective assessment of leaf blast disease. Keywords: Disease classification, Lesion identification, Maximum interclass variance method, Rice blast, Stepwise regression.

Organizational Factors as Predictors of Teachers' Burnout

2001 ◽  
Vol 88 (3) ◽  
pp. 627-634 ◽  
Author(s):  
Athanasios Koustelios
Keyword(s):  
Regression Analysis ◽  
Role Conflict ◽  
Emotional Exhaustion ◽  
Role Ambiguity ◽  
Stepwise Regression ◽  
Maslach Burnout Inventory ◽  
Organizational Factors ◽  
Stepwise Regression Analysis ◽  
Personal Accomplishment ◽  
Highly Correlated

The purpose of this study was to examine the burnout experienced by a sample of Greek teachers and to explore the extent to which certain organizational factors predict teachers' scores on the Maslach Burnout Inventory. The sample consisted of 100 teachers, 28 to 59 years of age. Greek teachers' means were lower than those for burnout of U.S. teachers. Stepwise regression analysis identified satisfaction with the job itself was the only significant predictor for Depersonalization and Emotional Exhaustion subscales, while satisfaction with the job itself and satisfaction with promotion were significant predictors for the Personal Accomplishment subscale. These findings showed that stress, e.g., role conflict and role ambiguity, were not highly correlated with teachers' burnout.

scholarly journals Residents’ Diachronic Perception of the Impacts of Ecological Resettlement in a World Heritage Site

2019 ◽  
Vol 16 (19) ◽  
pp. 3556
Author(s):  
Kai Wang ◽  
Menghan Wang ◽  
Chang Gan ◽  
Mihai Voda
Keyword(s):  
Regression Analysis ◽  
Stepwise Regression ◽  
World Heritage ◽  
Stepwise Regression Analysis ◽  
World Heritage Site ◽  
The Sustainable Development ◽  
Multiple Stepwise Regression Analysis ◽  
Heritage Site ◽  
Negative Perceptions ◽  
Main Factors

As one of the main factors in any tourist destination, residents’ perception of the impacts of ecological resettlement has a substantial influence on the sustainable development of any world heritage site. Our research takes the residents of three different resettlement locations in the Wulingyuan scenic area, a world heritage site, as the object of our survey. Based on questionnaire investigations in 2010 and 2016, this article analyzes the residents’ diachronic perception of the impacts of ecological resettlement. Independent sample t-tests and Analysis of Variance (ANOVA) are used to compare the differences in residents’ perception toward ecological relocation and analyse how demographic characteristics affect residents’ perception. Multiple stepwise regression analysis is applied to explore the main factors that contribute to the differences in the perception of impacts of ecological resettlement. The results show that during the study period, respondents have the strongest perceptions of the economic, socio-cultural, resource-environment and psychological impacts. However, they have negative perceptions of relocation policy impacts. Compared with 2010, residents with different gender, age, education level, income level and engagement in tourism have significant differences in perception of impacts of resettlement in 2016. Multiple stepwise regression analysis demonstrates that the perceptions of impacts of the ecological resettlement and economic policy are the primary factors to affect residents’ overall perceptions.

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