Detecting Parkinson's disease from sustained phonation and speech signals.

This study investigates signals from sustained phonation and text-dependent speech modalities for Parkinson's disease screening. Phonation corresponds to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. Signals were recorded through two channels simultaneously, namely, acoustic cardioid (AC) and smart phone (SP) microphones.

Automatic detection and morphological delineation of bacteriophages in electron microscopy images.

Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.

Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation: acoustic versus contact microphone.

Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets.

An adaptive panoramic filter bank as a qualitative model of the filtering system of the cochlea: the peculiarities in linear and nonlinear mode.

Outer hair cells in the cochlea of the ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of the PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of the PF along the basilar membrane.

Categorizing normal and pathological voices: automated and perceptual categorization.

Objectives: The aims of the present study were to evaluate the accuracy of an elaborated automated voice categorization system that classified voice signal samples into healthy and pathological classes and to compare it with classification accuracy that was attained by human experts.

Material And Methods: We investigated the effectiveness of 10 different feature sets in the classification of voice recordings of the sustained phonation of the vowel sound /a/ into the healthy and two pathological voice classes, and proposed a new approach to building a sequential committee of support vector machines (SVMs) for the classification. By applying "genetic search" (a search technique used to find solutions to optimization problems), we determined the optimal values of hyper-parameters of the committee and the feature sets that provided the best performance.

Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders.

Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data.

Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used.

Towards noninvasive screening for malignant tumours in human larynx.

This article is concerned with soft computing-based noninvasive screening for malignant disorders in human larynx. The suitability of two types of data for the analysis is explored. The questionnaire data and the digital voice recordings of the sustained phonation of the vowel sound /a/ are the data types considered in this study.

Advances in laryngeal imaging.

Imaging and image analysis became an important issue in laryngeal diagnostics. Various techniques, such as videostroboscopy, videokymography, digital kymography, or ultrasonography are available and are used in research and clinical practice. This paper reviews recent advances in imaging for laryngeal diagnostics.

Using the patient's questionnaire data to screen laryngeal disorders.

This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied.

Quantitative microlaryngoscopic measurements of vocal fold polyps, glottal gap and their relation to vocal function.

Objectives: The purpose of this study was to quantify the size of vocal fold polyps and to investigate the relationship between the glottal gap and parameters of acoustic voice analysis and phonetography.

Material And Methods: Eighty-one microlaryngoscopic images and digital recordings of voices (acoustic analysis and phonetogram) acquired from the patients with vocal fold polyps (VFPs) were employed in this study. Vocal fold (VF) images were collected during routine direct microlaryngoscopy using Moller-Wedel Universa 300 surgical microscope, 3-CCD Elmo 768 x 576-pixel color video camera and a 300 W Xenon light source.

Automated speech analysis applied to laryngeal disease categorization.

The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases.

A kernel-based approach to categorizing laryngeal images.

This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes.

Physiologically inspired signal preprocessing for auditory prostheses: insights from the electro-motility of the OHC.

We designed a non-linear functional model of the outer hair cell (OHC) functioning in the filtering system of the cochlea and then isolated from it two second-order structures, one employing the mechanism of the somatic motility and the other the hair bundle motion of the OHC. The investigation of these circuits showed that the main mechanism increasing the sensitivity and frequency selectivity of the filtering system is the somatic motility. The mechanism of the active hair bundle motion appeared less suitable for realization of the band-pass filtering structures due to the dependence of the sensitivity, natural frequency and selectivity on the signal intensity.

Multiple feature sets based categorization of laryngeal images.

This paper is concerned with an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular, and diffuse. The problem is treated as an image analysis and classification task. Aiming to obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted.

Towards a computer-aided diagnosis system for vocal cord diseases.

Objective: The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords.

Methodology: The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used.

Compression, adaptation and efferent control in a revised outer hair cell functional model.

In the cochlea of the inner ear, outer hair cells (OHC) together with the local passive structures of the tectorial and basilar membranes comprise non-linear resonance circuits with the local and central (afferent-efferent) feedback. The characteristics of these circuits and their control possibilities depend on the mechanomotility of the OHC. The main element of our functional model of the OHC is the mechanomotility circuit with the general transfer characteristic y=ktanh(x-a).

A multi-channel adaptive nonlinear filtering structure realizing some properties of the hearing system.

An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained.

A non-linear circuit for simulating OHC of the cochlea.

In the present paper, referring to known characteristics of the outer hair cells functioning in the cochlea of the inner ear, a functional model of the outer hair cells is constructed. It consists of a linear feed-forward circuit and a non-linear positive feedback circuit. The feed-forward circuit reflects the contribution of local basilar and tectorial membrane areas and passive outer hair cells' physical parameters to the forming of low-selectivity resonance characteristics.