10 research outputs found
COMPARISON OF SELECTED CLASSIFICATION METHODS BASED ON MACHINE LEARNING AS A DIAGNOSTIC TOOL FOR KNEE JOINT CARTILAGE DAMAGE BASED ON GENERATED VIBROACOUSTIC PROCESSES
Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods
Acoustic Monitoring of Joint Health
The joints of the human body, especially the knees, are continually exposed to varying loads as a person goes about their day. These loads may contribute to damage to tissues including cartilage and the development of degenerative medical conditions such as osteoarthritis (OA). The most commonly used method currently for classifying the severity of knee OA is the Kellgren and Lawrence system, whereby a grade (a KL score) from 0 to 4 is determined based on the radiographic evidence. However, radiography cannot directly depict cartilage damage, and there is low inter-observer precision with this method. As such, there has been a significant activity to find non-invasive and radiation-free methods to quantify OA, in order to facilitate the diagnosis and the appropriate course of medical action and to validate the development of therapies in a research or clinical setting. A number of different teams have noted that variation in knee joint sounds during different loading conditions may be indicative of structural changes within the knee potentially linked to OA. Here we will review the use of acoustic methods, such as acoustic Emission (AE) and vibroarthrography (VAG), developed for the monitoring of knee OA, with a focus on the issues surrounding data collection and analysis
APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT
The paper presents results of preliminary research of analysis of signals recorded for open and closed kinematic chain in one volunteer with chon-dromalacia in both knees. The preliminary research was conducted in order to establish the accuracy of the proposed method and will be used for for-mulating further research areas. The aim of the paper is to show how FFT, recurrence plots and recurrence quantification analysis (RQA) can help in bioacoustic signals analysis
Advanced analyses of physiological signals and their role in Neonatal Intensive Care
Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity
Design of Machine Learning Algorithms with Applications to Breast Cancer Detection
Machine learning is concerned with the design and development of algorithms and
techniques that allow computers to 'learn' from experience with respect to some class
of tasks and performance measure. One application of machine learning is to improve
the accuracy and efficiency of computer-aided diagnosis systems to assist physician,
radiologists, cardiologists, neuroscientists, and health-care technologists. This thesis
focuses on machine learning and the applications to breast cancer detection. Emphasis
is laid on preprocessing of features, pattern classification, and model selection.
Before the classification task, feature selection and feature transformation may be
performed to reduce the dimensionality of the features and to improve the classification
performance. Genetic algorithm (GA) can be employed for feature selection based
on different measures of data separability or the estimated risk of a chosen classifier.
A separate nonlinear transformation can be performed by applying kernel principal
component analysis and kernel partial least squares.
Different classifiers are proposed in this work: The SOM-RBF network combines
self-organizing maps (SOMs) and radial basis function (RBF) networks, with the RBF
centers set as the weight vectors of neurons from the competitive layer of a trained
SaM. The pairwise Rayleigh quotient (PRQ) classifier seeks one discriminating boundary
by maximizing an unconstrained optimization objective, named as the PRQ criterion,
formed with a set of pairwise const~aints instead of individual training samples.
The strict 2-surface proximal (S2SP) classifier seeks two proximal planes that are not
necessary parallel to fit the distribution of the samples in the original feature space or
a kernel-defined feature space, by ma-ximizing two strict optimization objectives with
a 'square of sum' optimization factor. Two variations of the support vector data description
(SVDD) with negative samples (NSVDD) are proposed by involving different
forms of slack vectors, which learn a closed spherically shaped boundary, named as the
supervised compact hypersphere (SCH), around a set of samples in the target class. \Ve
extend the NSVDDs to solve the multi-class classification problems based on distances
between the samples and the centers of the learned SCHs in a kernel-defined feature
space, using a combination of linear discriminant analysis and the nearest-neighbor rule.
The problem of model selection is studied to pick the best values of the hyperparameters
for a parametric classifier. To choose the optimal kernel or regularization
parameters of a classifier, we investigate different criteria, such as the validation error
estimate and the leave-out-out bound, as well as different optimization methods, such
as grid search, gradient descent, and GA. By viewing the tuning problem of the multiple
parameters of an 2-norm support vector machine (SVM) as an identification problem
of a nonlinear dynamic system, we design a tuning system by employing the extended
Kalman filter based on cross validation. Independent kernel optimization based on
different measures of data separability are a~so investigated for different kernel-based
classifiers.
Numerous computer experiments using the benchmark datasets verify the theoretical
results, make comparisons among the techniques in measures of classification
accuracy or area under the receiver operating characteristics curve. Computational
requirements, such as the computing time and the number of hyper-parameters, are
also discussed.
All of the presented methods are applied to breast cancer detection from fine-needle
aspiration and in mammograms, as well as screening of knee-joint vibroarthrographic
signals and automatic monitoring of roller bearings with vibration signals. Experimental
results demonstrate the excellence of these methods with improved classification
performance.
For breast cancer detection, instead of only providing a binary diagnostic decision
of 'malignant' or 'benign', we propose methods to assign a measure of confidence
of malignancy to an individual mass, by calculating probabilities of being benign and
malignant with a single classifier or a set of classifiers
Advances in Sensors and Sensing for Technical Condition Assessment and NDT
The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology
Features dataset accompanied with the manuscript "Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals"
Features dataset accompanied with the manuscript: "Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals" by Krzysztof Kręcisz, Dawid Bączkowicz