George Manis

Heartbeat Time Series Classification With Support Vector Machines

In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.

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%).

Fast computation of approximate entropy

The approximate entropy (ApEn) is a measure of systems complexity. The implementation of the method is computationally expensive and requires execution time analogous to the square of the size of the input signal. We propose here a fast algorithm which speeds up the computation of approximate entropy by detecting early some vectors that are not similar and by excluding them from the similarity test. Experimental analysis with various biomedical signals revealed a significant improvement in execution times.

Experimental analysis of heart rate variability of long-recording electrocardiograms in normal subjects and patients with coronary artery disease and normal left ventricular function

The heart rate signal contains valuable information about cardiac health, which cannot be extracted without the use of appropriate computerized methods. This paper presents an analysis of various electrocardiograms, the aim of which is to categorize them into two distinct groups. Group A represents young male subjects with no prior occurrence of coronary disease events and Group B represents middle-aged male subjects who have symptomatic coronary artery disease without myocardial infarction and whose 12-lead ECGs do not contain any abnormalities, thus wrongly indicating a normal subject. Electrocardiographic recordings are approximately 2h in length and acquired under conditions that favor the stationarity of collected data. Linear and nonlinear characteristics are studied by applying several techniques including Fourier analysis, Correlation Dimension Estimation, Approximate Entropy, and the Discrete Wavelet Transform. The small variations of the diagnostic information given by each one of the methods as well as the slightly different conclusions among similar studies indicate the necessity of further investigation, combined use, and complementary application of different approaches.

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.

BLOCK ENTROPY ANALYSIS OF HEART RATE VARIABILITY SIGNALS

In this paper we present a novel approach to the analysis of Heat Rate Variability (HRV) data, by coarse-graining analysis using the estimation of Block Entropies with the technique of lumping. HRV time series are generated from long recordings of Electrocardiograms (ECGs) and are then filtered in order to produce a coarse-grained symbolic dynamics. Block Entropy analysis is applied to these dynamics in order to examine its coarse-grained statistics. Our data set is comprised of two subsets, one of healthy subjects and another of Coronary Artery Disease (CAD) patients. It is found that Entropy analysis provides a quick and efficient tool for the differentiation of these series according to subject category. Healthy subjects provided more complex statistics compared to patients; specifically, the healthy data files provided higher values of block Entropies compared to patient ones. We also compare these results with the Correlation Dimension Estimation in order to establish coherency. We believe that this analysis may provide a useful statistical method towards the better understanding of the human cardiac system.

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.

Automatic generation of portable parallel natural language parsers

Since natural language parsing is a computationally intensive task, the parallel parsing of natural language seems a promising choice. This paper describes both the Eu-PAGE (Eurotra PArser GEnerator) meta-compiler for the Eurotra formalism, which is a tool that automatically generates parallel natural language parsers, and the Dialogos parser, which is a parallel parser for the Greek language generated by Eu-PAGE. Parallel parsers generated by Eu-PAGE are based on finite-state machines, employ coarse-grained parallelism and are portably implemented on top of two parallel software platforms: PVM and Orchid. Orchid uses light-weight processes as the basic unit of parallelism, enhanced with advanced operating system facilities. The collected experimental results so far demonstrate satisfactory speed-ups of the parallel implementations compared to the sequential one.

Time Dependent Fuzzy Cognitive Maps for Medical Diagnosis

Time dependence in medical diagnosis is important since, frequently, symptoms evolve over time, thus, changing with the progression of an illness. Taking into consideration that medical information may be vague, missing and/or conflicting during the diagnostic procedure, a new type of Fuzzy Cognitive Maps (FCMs), the soft computing technique that can handle uncertainty to infer a result, have been developed for Medical Diagnosis. Here, a method to enhance the FCM behaviour is proposed introducing time units that can follow disease progression. An example from the pulmonary field is described.

Parallel approaches to piecewise linear approximation

Abstract Two parallel algorithms have been developed for the piecewise linear approximation (PLA) of digitised curves. The first one is a new general purpose PLA algorithm, based on certain improvements of a serial algorithm. The second one is a peak preserving PLA algorithm particularly suited for the ECG waveform approximation. Both algorithms have been fully implemented, tested and evaluated on a distributed memory parallel architecture, using the OCCAM language. The derived results for both algorithms are encouraging, since they lead to optimal curve approximations, and they are amenable to real-time PLA applications.