Evanthia E. Tripoliti

Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors

Tremor is the most common motor disorder of Parkinsons disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patients body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

Automatic detection of freezing of gait events in patients with Parkinson's disease

The aim of this study is to detect freezing of gait (FoG) events in patients suffering from Parkinsons disease (PD) using signals received from wearable sensors (six accelerometers and two gyroscopes) placed on the patients body. For this purpose, an automated methodology has been developed which consists of four stages. In the first stage, missing values due to signal loss or degradation are replaced and then (second stage) low frequency components of the raw signal are removed. In the third stage, the entropy of the raw signal is calculated. Finally (fourth stage), four classification algorithms have been tested (Naïve Bayes, Random Forests, Decision Trees and Random Tree) in order to detect the FoG events. The methodology has been evaluated using several different configurations of sensors in order to conclude to the set of sensors which can produce optimal FoG episode detection. Signals recorded from five healthy subjects, five patients with PD who presented the symptom of FoG and six patients who suffered from PD but they do not present FoG events. The signals included 93 FoG events with 405.6s total duration. The results indicate that the proposed methodology is able to detect FoG events with 81.94% sensitivity, 98.74% specificity, 96.11% accuracy and 98.6% area under curve (AUC) using the signals from all sensors and the Random Forests classification algorithm.

A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data

The aim of this work is to present an automated method that assists in the diagnosis of Alzheimers disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time series using a Generalized Linear Model, (c) feature extraction from the fMRI data, (d) feature selection, (e) classification using classical and improved variations of the Random Forests algorithm and Support Vector Machines, and (f) conversion of the trees, of the Random Forest, to rules which have physical meaning. The method is evaluated using a dataset of 41 subjects. The results of the proposed method indicate the validity of the method in the diagnosis (accuracy 94%) and monitoring of the Alzheimers disease (accuracy 97% and 99%).

Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm

The accurate diagnosis of diseases with high prevalence rate, such as Alzheimer, Parkinson, diabetes, breast cancer, and heart diseases, is one of the most important biomedical problems whose administration is imperative. In this paper, we present a new method for the automated diagnosis of diseases based on the improvement of random forests classification algorithm. More specifically, the dynamic determination of the optimum number of base classifiers composing the random forests is addressed. The proposed method is different from most of the methods reported in the literature, which follow an overproduce-and-choose strategy, where the members of the ensemble are selected from a pool of classifiers, which is known a priori. In our case, the number of classifiers is determined during the growing procedure of the forest. Additionally, the proposed method produces an ensemble not only accurate, but also diverse, ensuring the two important properties that should characterize an ensemble classifier. The method is based on an online fitting procedure and it is evaluated using eight biomedical datasets and five versions of the random forests algorithm (40 cases). The method decided correctly the number of trees in 90% of the test cases.

A supervised method to assist the diagnosis and monitor progression of Alzheimer's disease using data from an fMRI experiment

OBJECTIVEnThe aim of this work is to provide a supervised method to assist the diagnosis and monitor the progression of the Alzheimers disease (AD) using information which can be extracted from a functional magnetic resonance imaging (fMRI) experiment.nnnMETHODS AND MATERIALSnThe proposed method consists of five stages: (a) preprocessing of fMRI data, (b) modeling of the fMRI voxel time series using a generalized linear model, (c) feature extraction from the fMRI experiment, (d) feature selection, and (e) classification using the random forests algorithm. In the last stage we employ features that were extracted from the fMRI and other features such as demographics, behavioral and volumetric measures. The aim of the classification is twofold: first to diagnose AD and second to classify AD as very mild and mild.nnnRESULTSnThe method is evaluated using data from 41 subjects. The stage of AD is established using the Washington University Alzheimers Disease Research Center recruitment and assessment procedures. The method classifies a patient as healthy or demented with 84% sensitivity and 92.3% specificity, and the stages of AD with 81% and 87% accuracy for the three class and the four class problem, respectively.nnnCONCLUSIONSnThe method is advantageous since it is fully automated and for the first time the diagnosis and staging of the disease are addressed using fMRI.

Editorial: Modifications of the construction and voting mechanisms of the Random Forests Algorithm

The aim of this work is to propose modifications of the Random Forests algorithm which improve its prediction performance. The suggested modifications intend to increase the strength and decrease the correlation of individual trees of the forest and to improve the function which determines how the outputs of the base classifiers are combined. This is achieved by modifying the node splitting and the voting procedure. Different approaches concerning the number of the predictors and the evaluation measure which determines the impurity of the node are examined. Regarding the voting procedure, modifications based on feature selection, clustering, nearest neighbors and optimization techniques are proposed. The novel feature of the current work is that it proposes modifications, not only for the improvement of the construction or the voting mechanisms but also, for the first time, it examines the overall improvement of the Random Forests algorithm (a combination of construction and voting). We evaluate the proposed modifications using 24 datasets. The evaluation demonstrates that the proposed modifications have positive effect on the performance of the Random Forests algorithm and they provide comparable, and, in most cases, better results than the existing approaches.

Automated segmentation and quantification of inflammatory tissue of the hand in rheumatoid arthritis patients using magnetic resonance imaging data

OBJECTIVESnThe aim of this paper is the development of an automated method for the segmentation and quantification of inflammatory tissue of the hand in patients suffering form rheumatoid arthritis using contrast enhanced T1-weighted magnetic resonance images.nnnMETHODS AND MATERIALSnThe proposed automatic method consists of four stages: (a) preprocessing of images, (b) identification of the number of clusters, by minimizing the appropriate validity index, (c) segmentation using the fuzzy C-means algorithm employing four features which are related to intensity and the location of pixels and (d) postprocessing, where defuzzification is performed and small objects and vessels are eliminated and quantification takes place.nnnRESULTSnThe proposed method is evaluated using a dataset of image sequences obtained from 25 patients suffering from rheumatoid arthritis. For 17 of them we have obtained follow-up images after 1 year treatment. The obtained sensitivity and positive predictive rate is 97.71% and 83.35%, respectively. In addition, quantification of inflammation before and after treatment, as well as, comparison with manual segmentation is carried out.nnnCONCLUSIONSnThe proposed method performs very well and results in high detection and quantification accuracy. However, the reduction of false positives and the identification of old inflammation must be addressed.

A supervised method to assist the diagnosis of Alzheimer's Disease based on functional Magnetic Resonance Imaging

In this work we present a supervised method to assist the diagnosis of Alzheimers disease (AD) based on functional magnetic resonance images (fMRI). The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling the way in which the BOLD response depends on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults).

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

A supervised method to assist the diagnosis and classification of the status of Alzheimer's disease using data from an fMRI experiment

The aim of this work is the development of a method to assist the diagnosis and classification of the status of Alzheimers Disease (AD) using information that can be extracted from fMRI. The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling BOLD response depending on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests (RF) algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults.