An ordinal classification approach for CTG categorization.

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

An intelligent assistant for physicians.

This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users' inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.

A one-class approach to cardiotocogram assessment.

Cardiotocogram (CTG) is the most widely used means for the assessment of fetal condition. CTG consists of two traces one depicting the Fetal Heart Rate (FHR), and the other the Uterine Contractions (UC) activity. Many automatic methods have been proposed for the interpretation of the CTG. Most of them rely either on a binary classification approach or on a multiclass approach to come up with a decision about the class that the tracing belongs to. This work investigates the use of a one-class approach to the assessment of CTGs building a model only for the healthy data. The preliminary results are promising indicating that normal traces could be used as part of an automatic system that can detect deviations from normality.

Grammatical Evolution for Features of Epileptic Oscillations in Clinical Intracranial Electroencephalograms.

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (~65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.