Voice Pathology Analysis using DT-CWPT and ReliefF Algorithm

The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

Speech is the underlying strong characteristic and voice is the vibration that the air creates as it's pushed out of the lungs and going into the vocal cords. Vocal cords are two folds of tissue within the larynx, sometimes called a speech box, the sound of these strings is what gives rise to speech.Anything that interferes with vocal cord movement or contact can cause a voice disorder. Speech production may often be compromised as social stressors contribute to chronic aphonia or dysphonia.Machine learning algorithm and non-invasive systems may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic analysis. A strong voice impairment dataset will help in recognition and classification ofincreasing number of voice disorders in and outside the Arab area. The database protocols for AVPD (Arabic voice pathology database) has been developed in a way to prevent previous data base deficiencies in widely used databases like MEEI (Massachusetts eye and ear infirmary) and SVD (Saarbrücken voice database).

Automatic voice pathology detection and classification using vocal tract area irregularity

Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, enabling the early detection of voice pathologies and the diagnosis of the type of pathology from which patients suffer. This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy. We extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using autocorrelation as features to detect and classify pathological samples. We also extracted the entropy for each frame of the voice signal after we normalized its values to be used as the features. These features were investigated in distinct frequency bands to assess the contribution of each band to the detection and classification processes. Various samples of the sustained vowel /a/ for both normal and pathological voices were extracted from three different databases in English, German, and Arabic. A support vector machine was used as a classifier. We also performed u-Tests to investigate if there is a significant difference between the means of the normal and pathological samples. The best achieved accuracies in both detection and classification varied depending on the used band, method, and database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. The highest obtained accuracies in the case of detection were 99.69%, 92.79%, and 99.79% for Massachusetts eye and ear infirmary (MEEI), Saarbrücken voice database (SVD), and Arabic voice pathology database (AVPD), respectively. However, the highest achieved accuracies for classification were 99.54%, 99.53%, and 96.02% for MEEI, SVD, and AVPD, correspondingly, using the combined feature.

ObjectiveTo investigate changes in the age of occurrence of herpes zoster ophthalmicus (HZO) in patients presenting to the Massachusetts Eye and Ear Infirmary (MEEI) from 2007 through 2013.DesignRetrospective chart review.SettingAcademic...

In order to extract the week fault features contained in the vibration signal of the mechanical equipment, a new de-nosing method based on dual-tree complex wavelet packet transform (DTCWPT) and principal manifold was proposed. Firstly, the vibration signals were decomposed into several sub-frequency bands by DTCWPT, the Shannon entropy was used to seek the best basis of DTCWPT, and a new adaptive threshold function was employed to denoise the wavelet packet coefficients on the best basis of the real part and imaginary part of DTCWPT via the de-noising criterion, then the wavelet packet coefficients were reconstructed into a high dimensional space. Secondly, t-distributed stochastic neighbor embedding (t-SNE) was performed to extract a low dimensional manifold, the proposed threshold function was further applied to process the low dimensional manifold, aiming at separate the signal and noise, the principal manifold was reconstructed by the method of spectral regression analysis, thus, the signals were reconstructed back into one dimensional time series after eliminating the noise. Finally, a simulated signal and a real bearing fault signal were used to validate the proposed method, the results have demonstrated that the proposed method has a good performance of nonlinear noise reduction, and can extract fault features of rolling bearing effectively.

Chapter 15 Auditory-Nerve Activity in Cats with Normal and Abnormal Cochleas N. Y. S. Kiang, N. Y. S. Kiang Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this authorE. C. Moxon, E. C. Moxon Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this authorR. A. Levine, R. A. Levine Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this author N. Y. S. Kiang, N. Y. S. Kiang Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this authorE. C. Moxon, E. C. Moxon Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this authorR. A. Levine, R. A. Levine Eaton-Peabody Laboratory of Auditory Physiology, Massachusetts Eye and Ear Infirmary, Research Laboratory of Electronics and Department of Electrical Engineering, Massachusetts Institute of Technology; and Harvard Medical SchoolSearch for more papers by this author Book Editor(s):G. E. W. Wolstenholme, G. E. W. WolstenholmeSearch for more papers by this authorJulie Knight, Julie KnightSearch for more papers by this author First published: 01 January 1970 https://doi.org/10.1002/9780470719756.ch15Citations: 109Book Series:Novartis Foundation Symposia AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Ciba Foundation Symposium - Sensorineural Hearing Loss RelatedInformation

Assessing Outcomes in Facial Reanimation: Evaluation and Validation of the SMILE System for Measuring Lip Excursion During Smiling

Journal of MorphologyVolume 107, Issue 2 p. 123-140 Article The vocal organ of the Howling monkey (Alouatta palliata)† G. Kelemen, G. Kelemen Department of Otolaryngology, Harvard Medical School, and The Massachusetts Eye and Ear Infirmary, Boston, MassachusettsSearch for more papers by this authorJ. Sade, J. Sade Department of Otolaryngology, Harvard Medical School, and The Massachusetts Eye and Ear Infirmary, Boston, MassachusettsSearch for more papers by this author G. Kelemen, G. Kelemen Department of Otolaryngology, Harvard Medical School, and The Massachusetts Eye and Ear Infirmary, Boston, MassachusettsSearch for more papers by this authorJ. Sade, J. Sade Department of Otolaryngology, Harvard Medical School, and The Massachusetts Eye and Ear Infirmary, Boston, MassachusettsSearch for more papers by this author First published: September 1960 https://doi.org/10.1002/jmor.1051070202Citations: 34 † Aided by a grant from the Wenner-Gren Foundation for Anthropological Research. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume107, Issue2September 1960Pages 123-140 RelatedInformation

The LaryngoscopeVolume 72, Issue 9 p. 1142-1157 Article The pathology of sudden deafness† Harold F. Schuknecht M.D., Harold F. Schuknecht M.D. Boston, Mass. Chief, Department of Otolaryngology, Massachusetts Eye and Ear Infirmary; Lecompte Professor of Otology and Professor of Laryngology, Harvard Medical School; Formerly Otologist, Henry Ford Hospital, Detroit.Search for more papers by this authorJaime Benitez M.D., Jaime Benitez M.D. Mexico City, Mexico D. F. Instructor, Department of Otolaryngology, University of Mexico Medical School; Formerly Research Fellow at the Otological Research Laboratory, Henry Ford Hospital, Detroit.Search for more papers by this authorJan Beekhuis M.D., Jan Beekhuis M.D. Detroit, Mich. Associate Professor, Department of Otolaryngology, Wayne State University Medical School, Detroit.Search for more papers by this authorMakoto Igarashi M.D., Makoto Igarashi M.D. Boston, Mass. Otology Research Fellow, Massachusetts Eye and Ear Infirmary, Boston.Search for more papers by this authorGeorge Singleton M.D., George Singleton M.D. Gainsville, Ga. Associate Professor and Chief, Department of Otolaryngology, University of Florida School of Medicine, Gainsville.Search for more papers by this authorLuzius Ruedi M.D., Luzius Ruedi M.D. Zurich, Switzerland Professor of Otorhinolaryngology, Director of the Ear, Nose and Throat Clinic, University of Zurich, Zurich, Switzerland.Search for more papers by this author Harold F. Schuknecht M.D., Harold F. Schuknecht M.D. Boston, Mass. Chief, Department of Otolaryngology, Massachusetts Eye and Ear Infirmary; Lecompte Professor of Otology and Professor of Laryngology, Harvard Medical School; Formerly Otologist, Henry Ford Hospital, Detroit.Search for more papers by this authorJaime Benitez M.D., Jaime Benitez M.D. Mexico City, Mexico D. F. Instructor, Department of Otolaryngology, University of Mexico Medical School; Formerly Research Fellow at the Otological Research Laboratory, Henry Ford Hospital, Detroit.Search for more papers by this authorJan Beekhuis M.D., Jan Beekhuis M.D. Detroit, Mich. Associate Professor, Department of Otolaryngology, Wayne State University Medical School, Detroit.Search for more papers by this authorMakoto Igarashi M.D., Makoto Igarashi M.D. Boston, Mass. Otology Research Fellow, Massachusetts Eye and Ear Infirmary, Boston.Search for more papers by this authorGeorge Singleton M.D., George Singleton M.D. Gainsville, Ga. Associate Professor and Chief, Department of Otolaryngology, University of Florida School of Medicine, Gainsville.Search for more papers by this authorLuzius Ruedi M.D., Luzius Ruedi M.D. Zurich, Switzerland Professor of Otorhinolaryngology, Director of the Ear, Nose and Throat Clinic, University of Zurich, Zurich, Switzerland.Search for more papers by this author First published: September 1962 https://doi.org/10.1288/00005537-196209000-00003Citations: 66 † Presented at the 65th Annual Meeting of the American Laryngological, Rhinological and Otological Society, Inc., Dallas, Tex., May 1, 1962. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume72, Issue9September 1962Pages 1142-1157 RelatedInformation

Journal Article Formulary of the Massachusetts General Hospital and the Massachusetts Eye & Ear Infirmary Get access Formulary of the Massachusetts General Hospital and the Massachusetts Eye & Ear Infirmary. 1951 Edition. Edited by John T. Murphy, Pharmacist-in-Chief and Prepared by the Pharmacy Committee. 155 pages, 4¼" × 5⅞", printed and paper bound. Available for $1.25 from Massachusetts General Hospital, Pharmacy Department, Boston 14, Mass. Gloria Niemeyer Gloria Niemeyer American Pharmaceutical Association Washington, D. C. Search for other works by this author on: Oxford Academic Google Scholar The Bulletin of the American Society of Hospital Pharmacists, Volume 9, Issue 2, 1 April 1952, Page 126, https://doi.org/10.1093/ajhp/9.2.126a Published: 01 April 1952

The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.

Heart disease is still one of the top causes of death globally, demanding early and effective prediction tools. Traditional predictive models frequently have limitations because they rely on single‐source statistics, which may not represent the entire scenario of a patient's complex health profile. Feature selection is an essential preprocessing step in machine learning that improves model efficiency by eliminating irrelevant or redundant features. This study evaluates the impact of feature selection on six classification modelsNaïve Bayes, Logistic Regression, Decision Tree, K‐Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forestacross four datasets. Performance was assessed using accuracy, precision, recall, F1‐score, and area under the curve (AUC) before and after feature selection. The results indicate that feature selection had varying effects across different models and datasets. In Dataset‐01, Random Forest maintained its strong performance (98.70% accuracy) even after feature selection, while SVM and KNN saw noticeable drops, with accuracy decreasing from 88.96% to 79.87% and 87.01% to 81.17% respectively. For Dataset‐02, SVM showed improvement, increasing from 80.00% to 85.00%, while Naïve Bayes and KNN also improved, reaching 82.50% and 82.50% accuracy, respectively. In Dataset‐03, Logistic Regression and KNN experienced declines, with accuracy dropping from 88.76% to 83.71% and 91.57% to 87.08%, respectively, while Random Forest fell from 97.75% to 91.57%. Dataset‐04 showed mixed results, with Logistic Regression improving from 86.67% to 91.11%, but KNN and Random Forest dropping from 97.78% to 84.44% and 91.11% to 82.22%, respectively. These findings suggest that while feature selection enhances performance for some models, it can negatively impact others, particularly those reliant on a larger feature set. Future research should explore adaptive feature selection techniques to optimize model performance across diverse datasets.

The Boston Keratoprosthesis: Highs and Lows of Intraocular Pressure and Outcomes

HighlightsE-nose and computer vision combined with data fusion strategies were applied to trace tea origins.Pearson correlation analysis, IG, and F-scores were applied to modify the fusion strategies.The classification performances of different fusion strategies were compared.The strategies of IG_SVM_FL and IG_SVM_DS achieved the best results.Abstract. The traceability of tea origins is of great significance. In this study, an electronic nose (E-nose) and computer vision system (CVS) were jointly applied to acquire aroma and image signals of tea samples, aiming at identifying Longjing teas from different geographic origins including Jinyun (120° 7' E, 28° 65' N), Xihu (120° 13' E, 30° 27' N), Xinchang (120° 9' E, 29° 50' N), and Qian Daohu (119° 3' E, 29° 60' N). Data fusion was used to integrate the E-nose and CVS signals for comprehensively characterizing the tea samples. Four traditional fusion strategies including k-nearest neighbors (KNN) and support vector machine (SVM) based feature-level fusion (KNN_FL and SVM_FL) and Dempster-Shafer (D-S) evidence theory based decision-level strategies (KNN_DS and SVM_DS) were applied for classification modeling. Pearson analysis, information gain (IG), and F-scores were employed to modify the traditional fusion strategies to reduce inconsistent and redundant information in the fusion process. The results indicated that the original fusion strategies had no superiority over independent E-nose and CVS decision-making. With the feature selection methods, the modified fusion strategies generally exhibited better performance than the independent decision-making and original fusion strategies. Moreover, the IG-based fusion strategies, encompassing IG_SVM_FL and IG_SVM_DS, achieved the highest classification accuracy of 100%. Keywords: Computer vision, Electronic nose, Feature selection, Fusion strategies, Tea origins.