Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Spectral evidence for three metal-linked ionization equilibria in the interaction of cobalt(II) horse liver alcohol dehydrogenase with coenzyme and substrate

The visible absorption bands in the region 525-575 nm of the catalytic cobalt ion in cobalt(II) horse liver alcohol dehydrogenase show characteristic pH-dependent changes both in the free enzyme and its complexes with nicotinamide adenine dinucleotide (NAD+) and NAD+ plus ethanol or 2,2,2-trifluoroethanol. In the free enzyme, the change of the coordination environment has an apparent pK of about 9.4. In the binary complex with NAD+ the spectral changes are complex, indicating changes in the coordination sphere in a lower pH range with an estimated pK value of about 7.9. The ternary complexes enzyme X NAD+ X ethanol and enzyme X NAD+ X 2,2,2-trifluoroethanol exhibit very similar, characteristic spectral features; their apparent pK values are 6.3 and less than 4, respectively. We ascribe these pK values to the ionization of the alcohol bound in the ternary complexes. The results demonstrate that the catalytic cobalt ion is sensing changes of the ionization state of the protein when going from low pH forms to high pH forms both in the absence and presence of coenzyme and substrate/inhibitor.     

Die Invasion der Stechmücken

Invasion of exotic mosquitos in Germany The scientists of the “German Mosquito Control Association” (KABS) and the University Heidelberg detected between 1995 and 2011 four new species of mosquitoes as neozoa for Germany. Aedes albopictus and Ochlerotatus j. japonicus have been introduced to new territories such as Germany in the context of globalization with trade goods, increased mobility of humans by means of vehicles. In particular the spread of the originally tropical species Ae. albopictus is favored by global warming. The rapid spread of Ae. albopictus and Oc. j. japonicus and the related risk of transmission of human pathogens, especially of arboviruses, increased the awareness of scientists, health authorities and the public and led to an intensive surveillance program for exotic mosquito species in Germany.