Alan Jovic

Feature Extraction for ECG Time-Series Mining Based on Chaos Theory

Chaos theory applied to ECG feature extraction is presented in this article. Several chaos methods, including phase space and attractors, correlation dimension, spatial filling index, central tendency measure and approximate entropy are explained in detail. A new feature extraction environment called ECG chaos extractor has been created in order to apply these chaos methods. System model and program functions are presented. Some of the obtained results are listed. Future work in this field of research is discussed.

A review of feature selection methods with applications

Feature selection (FS) methods can be used in data pre-processing to achieve efficient data reduction. This is useful for finding accurate data models. Since exhaustive search for optimal feature subset is infeasible in most cases, many search strategies have been proposed in literature. The usual applications of FS are in classification, clustering, and regression tasks. This review considers most of the commonly used FS techniques. Particular emphasis is on the application aspects. In addition to standard filter, wrapper, and embedded methods, we also provide insight into FS for recent hybrid approaches and other advanced topics.

Ontologies in Medical Knowledge Representation

In this work structure of medical ontologies and their construction process are presented. For each medical domain one has to specify the scope of the ontology, acquire medical knowledge, select a tool and an ontology language, design the ontology, and present it in an appropriate way. Special attention is devoted to the problem of representing relevant medical knowledge in the form of ontology. Connection of an ontology with a rule base as a part of a decision support system is established. The ontology of the heart failure disorder, designed for the use in Heartfaid platform, is used to explain the ontology construction process.

HRVFrame: Java-based framework for feature extraction from cardiac rhythm

Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework called HRVFrame implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different cardiac disorders. Some of the features implemented in the framework can also be applied to other biomedical time-series. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series.

Automatic Prediction of Falls via Heart Rate Variability and Data Mining in Hypertensive Patients: The SHARE Project Experience

Accidental falls in elderly is a major problem. This paper presents the preliminary results of a retrospective study investigating association between Heart Rate Variability (HRV) measures and risk of falling, analyzing 168 clinical 24- hour ECG recording from hypertensive patients, 47 of them experienced at least one fall in the three months before/after the registration. Several HRV patterns, based on 68 linear and non-linear HRV measures, were analyzed in relation to falls using advanced statistical and data mining methods.

Decision Tree Ensembles in Biomedical Time-Series Classification

There are numerous classification methods developed in the field of machine learning. Some of these methods, such as artificial neural networks and support vector machines, are used extensively in biomedical time-series classification. Other methods have been used less often for no apparent reason. The aim of this work is to examine the applicability of decision tree ensembles as strong and practical classification algorithms in biomedical domain. We consider four common decision tree ensembles: AdaBoost.M1+C4.5, Multi- Boost+C4.5, random forest, and rotation forest. The decision tree ensembles are compared with SMO-based support vector machines classifiers (linear, squared polynomial, and radial kernel) on three distinct biomedical time-series datasets. For evaluation purposes, 10x10-fold cross-validation is used and the classifiers are measured in terms of sensitivity, specificity, and speed of model construction. The classifiers are compared in terms of statistically significant winslosses-ties on the three datasets. We show that the overall results favor decision tree ensembles over SMO-based support vector machines. Preliminary results suggest that AdaBoost.M1 and MultiBoost are the best of the examined classifiers, with no statistically significant difference between them. These results should encourage the use of decision tree ensembles in biomedical time-series datasets where optimal model accuracy is sought.

Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features

The goal of this paper is to assess various combinations of heart rate variability (HRV) features in successful classification of four different cardiac rhythms. The rhythms include: normal, congestive heart failure, supraventricular arrhythmia, and any arrhythmia. We approach the problem of automatic cardiac rhythm classification from HRV by employing several features’ schemes. The schemes are evaluated using the random forest classifier. We extracted a total of 78 linear and nonlinear features. Highest results were achieved for normal/supraventricular arrhythmia classification (93%). A feature scheme consisting of: time domain (SDNN, RMSSD, pNN20, pNN50, HTI), frequency domain (Total PSD, VLF, LF, HF, LF/HF), SD1/SD2 ratio, Fano factor, and Allan factor features demonstrated very high classification accuracy, comparable to the results for all extracted features. Results show that nonlinear features have only minor influence on overall classification accuracy.

Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.

Accidental falls are a major problem of later life. Different technologies to predict falls have been investigated, but with limited success, mainly because of low specificity due to a high false positive rate. This Letter presents an automatic classifier based on heart rate variability (HRV) analysis with the goal to identify fallers automatically. HRV was used in this study as it is considered a good estimator of autonomic nervous system (ANS) states, which are responsible, among other things, for human balance control. Nominal 24 h electrocardiogram recordings from 168 cardiac patients (age 72 ± 8 years, 60 female), of which 47 were fallers, were investigated. Linear and nonlinear HRV properties were analysed in 30 min excerpts. Different data mining approaches were adopted and their performances were compared with a subject-based receiver operating characteristic analysis. The best performance was achieved by a hybrid algorithm, RUSBoost, integrated with feature selection method based on principal component analysis, which achieved satisfactory specificity and accuracy (80 and 72%, respectively), but low sensitivity (51%). These results suggested that ANS states causing falls could be reliably detected, but also that not all the falls were due to ANS states.

Extension and detailed overview of the HRVFrame framework for heart rate variability analysis

The analysis of heart rate variability (HRV) is an important diagnostic method for detection and assessment of cardiac abnormalities. The availability of complete computer frameworks that can aid researchers in the field of HRV analysis is limited due to the large number of different feature extraction methods. A recently developed framework for feature extraction from cardiac rhythm called HRVFrame is promising, because it allows a user to access more than 40 implemented linear and nonlinear methods. The aim of this paper is to provide a more detailed overview of this framework and all of its capabilities and recent extensions. Additionally, the aim is to encourage the use of HRVFrame as a free and open-source tool for developing medical applications based on Java programming language. A comparison of the framework with existing solutions for cardiac rhythm analysis is provided.

MULTISAB project: a web platform based on specialized frameworks for heterogeneous biomedical time series analysis - an architectural overview

The aim of this work is to present an architectural overview of a novel web platform used for heterogeneous biomedical time series analysis. Its architecture is based on three subprojects: frontend, backend, and processing. Frontend uses several contemporary web technologies to present a fast, responsive and pleasing user interface. Backend, written in Java, communicates with a database and with other servers, on which the processing subproject is deployed. The processing subproject contains several frameworks intended for: record input handling, signal preprocessing, data visualization, general time series features extraction, specific time series features extraction (e.g. heart rate variability and electroencephalogram), data mining, and reporting. The platform is in an early phase of implementation, but we demonstrate its features and capabilities, of which feature extraction frameworks and signal visualization currently stand out.