C. Druzgalski

Acoustics based assessment of respiratory diseases using GMM classification

The focus of this paper is to present a method utilizing lung sounds for a quantitative assessment of patient health as it relates to respiratory disorders. In order to accomplish this, applicable traditional techniques within the speech processing domain were utilized to evaluate lung sounds obtained with a digital stethoscope. Traditional methods utilized in the evaluation of asthma involve auscultation and spirometry, but utilization of more sensitive electronic stethoscopes, which are currently available, and application of quantitative signal analysis methods offer opportunities of improved diagnosis. In particular we propose an acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) which should assist in broader analysis, identification, and diagnosis of asthma based on the frequency domain analysis of wheezing and crackles.

Quantitative models for assessment of respiratory diseases

Experienced dynamic demographic growth of many cities, especially in the last three decades, has often resulted in a replacement of areas with vegetation and crops by industrial parks and residential areas, exposing areas to erosion. Mexicali, which is located in an arid region, is experiencing such transformations which are paralleled by a significant increase in respiratory diseases and allergies especially in children. In particular, adverse health effects of PM10 particles and asthma can be observed. The focus of this paper is to present a method for a quantitative assessment of patient health as it relates to respiratory disorders utilizing lung sounds. In order to accomplish this, applicable traditional techniques within the speech processing domain were utilized to evaluate lung sounds obtained with a digital stethoscope. Traditional methods utilized in the evaluation of asthma involve auscultation and spirometry, but utilization of more sensitive electronic stethoscopes, which are currently available, and application of quantitative signal analysis methods offer opportunities of improved diagnosis. In particular we propose acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) which should assist in broader analysis, identification, and diagnosis of asthma based on the frequency domain analysis of wheezing and crackles.

Expanded quantitative models for assessment of respiratory diseases and monitoring

The industrial and demographic expansion and associated increased exposure to pollutants continue to be critical factors contributing to the development of respiratory and cardiovascular diseases. Specifically, this paper builds on previously developed quantitative models for assessment of respiratory disorders utilizing acoustical characterization, as auscultation is a primary method used in initial assessment of respiratory and cardiovascular functions. Applicable techniques used in the speech processing domain were utilized to evaluate lung sound signals obtained with a digital stethoscope. Utilization of more sensitive electronic stethoscopes and application of quantitative signal analysis methods offer opportunities for improved diagnosis in children and overall patient monitoring. Reported methodology is based on expanded Gaussian Mixed Models (GMM). These expanded models provide significantly increased levels of peculiar respiratory signal identification reaching over the 92% level, although it is accomplished at higher computational demand. This approach allows broader quantitative analysis, identification and monitoring of respiratory disorders in general.

Modified classification of normal Lung Sounds applying Quantile Vectors

In this paper a novel Lung Sound Automatic Verification (LSAV) system and front-end Quantile based acoustic models to classify Lung Sounds (LS) are proposed. The utilization of Quantiles allowed an easier and objective assessment with smaller computational demand. Moreover, less-complex Gaussian Mixture Models (GMM) were computed than those previously reported. The LSAV system allowed us to reach practically negligible error in healthy (normal) LS verification. LASV system efficiency and the optimal GMMs were evaluated by using Equal Error Rate (EER) and Bayesian Information Criterion (BIC) techniques respectively. These approaches could provide a tool for broader medical evaluation which does not rely, as it is often the case, on a qualitative and subjective description of LS.In this paper a novel Lung Sound Automatic Verification (LSAV) system and front-end Quantile based acoustic models to classify Lung Sounds (LS) are proposed. The utilization of Quantiles allowed an easier and objective assessment with smaller computational demand. Moreover, less-complex Gaussian Mixture Models (GMM) were computed than those previously reported. The LSAV system allowed us to reach practically negligible error in healthy (normal) LS verification. LASV system efficiency and the optimal GMMs were evaluated by using Equal Error Rate (EER) and Bayesian Information Criterion (BIC) techniques respectively. These approaches could provide a tool for broader medical evaluation which does not rely, as it is often the case, on a qualitative and subjective description of LS.

Automated adipose tissue detection method

Obesity or adiposity is a major global health problem. Adipose tissue has been subdivided into two types: white adipose tissue (WAT) that stores energy, and brown adipose tissue (BAT) that functions to dissipate energy in the form of heat. BAT plays a particularly important role in newborns, which use this tissue to defend themselves against cold. However, using glucose tracer, it has been independently demonstrated that metabolically active BAT exists in adults. This has revived a scientific interest in BAT, targeting BAT as a new method to reduce obesity. However, BAT is extremely difficult to visualize, and even more to quantify. That is why we developed a mathematical model and intelligent software capable of automatically detecting and analyzing BAT, with the goal of quantifying it in the mammary adipose microenvironment. We utilized an animal model with abnormal mammary adipose tissue environment with increased amount of BAT in comparison with normal animals for this analysis. The proposed algorithm has two-fold function: visual and numerical. Visual function is expressed through detection and indication of the potential brown fat on the tissue microscopic images by marking it in a different color and distinguishing it from the rest of the tissue. Numerical function enables quantification of the amount of detected brown fat by calculating its numerical equivalent.

Quantile vectors based verification of normal lung sounds

Anthropogenic activities associated to population growth impact overall health and contribute to elevated rates of cardiovascular and respiratory diseases. In this paper we propose the Lung Sound Automatic Verification (LSAV), and other modalities to represent acoustic lung signals obtained by auscultation using a digital stethoscope. The utilization of quantiles allowed a) an easier and objective assessment with smaller computational demand, b) building of less-complex Gaussian Mixed Models (GMM) than those reported previously, and c) to reach practically negligible error in healthy LS verification. These approaches relate the lung sound energy to its characteristic frequency components, which in addition to a reliable verification technique simplified the normal lung sound recognition. Once the LS are evaluated, it would be possible to simplify classification if an individual auscultatory evaluation falls into the category of normal or abnormal indicators thus providing a tool for broader medical evaluation which does not rely, as it is often the case, on a qualitative and subjective description of these sounds.

Improved algorithm for mammary adipose microenvironment definition byl automated brown fat quantification

Adipose tissue, known also as fat, is a loose connective tissue that fills up space between the organs and provides structural and metabolic support. The classical functions of adipose tissue are the storage of energy in the form of triglycerides, and thermal insulation. Historically, adipose tissue has been subdivided into two main types: white fat that stores energy, and brown fat, that induce thermogenesis and produce heat. Brown fat was previously regarded as a special type of fat relevant only for newborns and hibernating animals defending against cold. Recently has been shown that brown fat is physiologically present and active in adult humans. However, brown fat is extremely difficult to visualize and even more difficult to quantify. We have found that levels of brown fat are dramatically increased in mammary tissue of the BRCA1 mutant mice compared to the normal, wild-type mice. We attempted to quantify the amount of brown fat in histological sections of mammary tissues from these mice. We initially presented a mathematical model and intelligent software capable of automatically detecting and quantifying the content of fat adipose tissue, with the goal of defining the mammary adipose microenvironment. The proposed algorithm showed itself to be very successful in detecting brown adipose tissue in images that contain pure mammary tissue. We propose here improved adipose tissue detection algorithm capable of successfully detecting brown fat in the whole mammary gland image that contains extra information like arteries, background and other surrounding elements. The proposed method has two fold function: visual and numerical. Visual function is expressed through detection and indication of the potential brown fat in the tissue microscopic images by marking it in a different color and distinguishing it from the rest of the tissue. Numerical function enables quantification of the amount of detected brown fat by calculating its numerical content equivalent.

Supplemental melanoma diagnosis for darker skin complexion gradients

Melanoma represents one of most malignant tumors associated with melanocytes in pigmented cells of the skin and in particular is a result of malignant transformation of melanocytes. Due to migration of neural cell crest, melanoma can develop not only on skin, but on oral and genital mucosa, and also gastrointestinal tract and brain. Melanoma is usually present and manifests itself with changes in color, size, contour and configuration, or may occur as new pigmented lesions. In particular, melanoma represents the sixth leading cause of malignancy in the United States with much higher mortality rate among non-Caucasian population, although is more common among whites. Considering its complexity, clinical diagnosis of melanoma is challenging even for experienced dermatologists. This is why it is necessary to develop computer assisted diagnostic tool for melanoma detection focused on dark and fair complexion skin which adds more objective judgments based on quantitative measures. Therefore, specialized algorithms were developed and tested utilizing databases including images of a variety of skin cancer manifestations. Those diagnostic indicators were assessed utilizing commonly used ABCDE criteria for different skin complexions and also natural and simulated darker background reflecting darker skin tones associated with different ethnic groups. Incorporated Canny, Prewitt, Roberts and Sobel edge detectors allowed to optimize melanoma diagnosis for darker skin tones and assess the degree of correct classification for each of ABCDE criterion reflecting varied skin complexion.

Multimodal classification of heart sounds attributes

Pollution and associated negative impacts on human health is one of the major concerns of the World Health Organization and healthcare providers. Current interests focus on particles suspended in air known as PM10 which significantly contribute to increased prevalence of heart disease. Specifically, the city of Mexicali was found to be one of the most polluted cities of Mexico in 2010. Cardiovascular abnormalities are often reflected in characteristic indicators of auscultation based examination. This fundamental diagnostic procedure can be significantly enhanced using low-cost detection technologies and accompanied pattern recognition for classification of associated sound attributes. Related economic issues are critical, both in Latin America and in other regions of the world, where often a limited level of specialized healthcare services are available. One of the goals of these studies was to prove initially demonstrated capabilities that the distinctive auscultatory classification indicators and diagnostic assessment can be easily implemented. In the case of heart sound signals, both the normal sounds and those representing abnormal conditions can be examined and differentiated for diagnostic purposes. The main focus of this study was to use Hidden Markov Models (HMM) for the classification and evaluation of heart sounds (HS). In particular, the application of HMM models provides greater robustness to noise and other interference such as the GMM models. The results demonstrate an enhanced quantitative evaluation, which could assist in a more accurate and economical HS assessment.

The HMM diagnostic models of respiratory sounds

Numerous studies including annual reports by Blacksmith Institute clearly document the magnitude of regional pollution and associated health risks. In particular the air pollution encompassing PM10 and smaller particles significantly contributes to the prevalence of respiratory diseases. Specifically, the city of Mexicali with a PM10 ranking of 137 in 2010 is considered as the most polluted city in Mexico largely due to the contribution of unusual environmental factors. Resulting respiratory abnormalities are often reflected in peculiar auscultatory indicators and their assessment can be accomplished using low cost technologies. These economic aspects are critical not only in Latin America but also other population centers globally considering the limited level of health services. Any classification of auscultatory indicators as reflected in lung sound (LS) characteristics needs to account for a noisy environment and the influence of heart sounds (HS). The aim of these studies was to utilize Hidden Markov Models (HMM) in light of the previously conducted assessment of lung sounds (LS) utilizing the Mixture Gaussians Models (GMM). In particular, the application of HMM models provides robustness to cope with noise and other interferences, to which the Mixture Gaussians Models (GMM) are more vulnerable. The conducted studies document that presented quantitative assessment of LS may add in more objective and economic scanning for respiratory abnormalities.