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New frontiers of analysis, interpretation and classification of biomedical signals: a computational intelligence framework

Gacek A.

Journal of Medical Informatics & Technologies

2011

Vol. 17

23--36

The methods of Computational Intelligence (CI) including a framework of Granular Computing, open promising research avenues in the realm of processing, analysis and interpretation of biomedical signals. Similarly, they augment the existing plethora of "classic" techniques of signal processing. CI comes as a highly synergistic environment in which learning abilities, knowledge representation, and global optimization mechanisms and this essential feature is of paramount interest when processing biomedical signals. We discuss the main technologies of Computational Intelligence (namely, neural networks, fuzzy sets, and evolutionary optimization), identify their focal points and elaborate on possible limitations, and stress an overall synergistic character, which ultimately gives rise to the highly symbiotic CI environment. The direct impact of the CI technology on ECG signal processing and classification is studied with a discussion on the main directions present in the literature. The design of information granules is elaborated on; their design realized on a basis of numeric data as well as pieces of domain knowledge is considered. Examples of the CI-based ECG signal processing problems are presented. We show how the concepts and algorithms of CI augment the existing classification methods used so far in the domain of ECG signal processing. A detailed construction of granular prototypes of ECG signals being more in rapport with the diversity of signals analyzed is discussed as well. ECG signals, Computational Intelligence, neurocomputing, fuzzy sets, information granules, Granular Computing, interpretation, classification, interpretability.

Price Prediction of the Electric Energy - Regression versus Neural Approach

Protasiewicz J., Szczepaniak P. S.

Journal of Applied Computer Science

2007

Vol. 15, nr 2

7-17

In this paper, two models for price prediction of the day ahead market are presented and evaluated. The work consists of two parts. The first part includes short description the day ahead market of electric energy exchange. In the second one, the regression and neural models applied. As an example, the polish power exchange market is used.