Unbiased Stereology. Three‐Dimensional Measurement in Microscopy.

We explored the neuropsychological profile for executive functions of children with attention deficit hyperactivity disorder (ADHD) to assess whether problems associated with the two most cited relevant processes-inhibition and attentional problems-were the core of any executive function difficulty. A battery of executive function tests was administered to 31 children with a clinical diagnosis of ADHD and to 33 normal control participants, all aged between 7 and 12. The executive function battery encompassed a number of tasks, selected because each had multiple measures: a sustained attention reaction time task, a related vigilance task, an adaptation of the Hayling Sentence Completion Test, an adaptation of the Brixton Spatial Rule Attainment Test, a Letter Fluency task, a number Stroop task, and an “n-back” working memory task. The overall pattern of the results fit well with those obtained in previous studies as far as abnormalities of the ADHD group in the domain of inhibitory processes, attentional functions, and executive functions. The children with ADHD, although performing well on baseline tasks, performed more poorly than the controls on all the experimental tasks with one borderline exception: Letter Fluency, where the children with ADHD showed a very different pattern than most adult frontal lobe subgroups. However, there was no specific impairment on measures of inhibitory processes. In addition, strategy generation and use were severely affected in the ADHD group. Particular findings fitted well with disorders of a high-level effort system and of a monitoring system.

Executive function profile of children with attention deficit hyperactivity disorder

The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various areas have been effectively solved by using FCM and its different variants. But, for efficient use of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains. Also, another intention of this survey is to encourage new researchers to make use of this simple algorithm (which is popularly called soft classification model) in problem solving.

Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014

Background 
Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established


Objective 
To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds This systematic review provides a complete summary of methods used in the literature to give a baseline for future works


Data sources 
A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases Additional articles were further obtained by references listed in the articles found Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification


Study selection 
Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated


Data extraction 
Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved


Data synthesis 
A total of 77 reports from the literature were included in this review 55 (7143%) of the studies focused on wheeze, 40 (5195%) on crackle, 9 (1169%) on stridor, 9 (1169%) on rhonchi, and 18 (2338%) on other sounds such as pleural rub, squawk, as well as the pathology Instrumentation used to collect data included microphones, stethoscopes, and accelerometers Several references obtained data from online repositories or book audio CD companions Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques Performance reported in the surveyed works were converted to accuracy measures for data synthesis


Limitations 
Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different A standard validation method has not been established, resulting in different works using different methods and performance measure definitions


Conclusion 
A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis The performance of recent studies showed a high agreement with conventional non-automatic identification This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases

/pdf/automatic-adventitious-respiratory-sound-analysis-a-3ikbv4aywn.pdf

Automatic adventitious respiratory sound analysis: A systematic review.

A new hybrid intelligent system for accurate detection of Parkinson's disease

Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds

A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist's visual perception In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades A high correlation between enzymes and Grey Relational Grades were found The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved

Quantitative Grading Using Grey Relational Analysis on Ultrasonographic Images of a Fatty Liver

In this study, we developed an expert diagnostic system for the interpretation of the portal vein Doppler signals belong the patients with cirrhosis and healthy subjects using signal processing and Artificial Neural Network (ANN) methods. Power spectral densities (PSD) of these signals were obtained to input of ANN using Short Time Fourier Transform (STFT) method. The four layered Multilayer Perceptron (MLP) training algorithms that we have built had given very promising results in classifying the healthy and cirrhosis. For prediction purposes, it has been presented that Levenberg Marquardt training algorithm of MLP network employing backpropagation works reasonably well. The diagnosis performance of the study shows the advantages of this system: It is rapid, easy to operate, noninvasive and not expensive. This system is of the better clinical application over others, especially for earlier survey of population. The stated results show that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.

Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

It is known that patients with Attention Deficit and Hyperactivity disorder (ADHD) and Conduct disorder (CD) commonly shows greater symptom severity than those with ADHD alone and worse outcomes. This study researches whether Default mode network (DMN) is altered in adolescents with ADHD + CD, relative to ADHD alone and controls or not. Ten medication-naive boys with ADHD + CD, ten medication-naive boys with ADHD and 10-age-matched typically developing (TD) controls underwent functional magnetic resonance imaging (fMRI) scans in the resting state and neuropsychological tasks such as the Wisconsin Card Sorting Test (WCST), Stroop Test TBAG Form (STP), Auditory Verbal learning Test (AVLT), Visual Auditory Digit Span B (VADS B) were applied to all the subjects included. fMRI scans can be used only nine patients in each groups. The findings revealed group differences between cingulate cortex and primary mortor cortex; cingulate cortex and somatosensory association cortex; angular gyrus (AG) and dorsal posterior cingulate cortex, in these networks increased activity was observed in participants with ADHD + CD compared with the ADHD. We found that lower resting state (rs)-activity was observed between left AG and dorsal posterior cingulate cortex, whereas higher rs-activity connectivity were detected between right AG and somatosensory association cortex in ADHD relative to the ones with ADHD + CD. In neuropsyhcological tasks, ADHD + CD group showed poor performance in WISC-R, WCST, Stroop, AVLT tasks compared to TDs. The ADHD + CD group displayed rs-functional abnormalities in DMN. Our results suggest that abnormalities in the intrinsic activity of resting state networks may contribute to the etiology of CD and poor prognosis of ADHD + CD.

Default mode network activity and neuropsychological profile in male children and adolescents with attention deficit hyperactivity disorder and conduct disorder

Semra Icer

Papers

Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds

Quantitative Grading Using Grey Relational Analysis on Ultrasonographic Images of a Fatty Liver

Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

Default mode network activity and neuropsychological profile in male children and adolescents with attention deficit hyperactivity disorder and conduct disorder