Temat: sygnał - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The Fuzzy Relevance Vector Machine and its application to noise reduction in ECG signal
Autorzy:: Momot, A.
Momot, M.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333828.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: systemy rozmyte
wnioskowanie bayesowskie
sygnał EKG
fuzzy systems
Bayesian inference
ECG signal
Opis:: The paper presents new method called the Fuzzy Relevance Vector Machine (FRVM), a modification of the relevance vector machine, introduced by M. Tipping, applied to learning Takagi-Sugeno-Kang (TSK) fuzzy system. Moreover it describes application of the FRVM to noise reduction in ECG signal. The results of the process are compared to those obtained using both Least Squares method for learning output functions in TSK rules and commonly used method using a low-pass moving average filter.
Źródło:: Journal of Medical Informatics & Technologies; 2005, 9; 99-105
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Empirical Bayesian averaging method and its application to noise reduction in ECG signal
Autorzy:: Momot, A.
Momot, M.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333575.pdf
Data publikacji:: 2006
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: sygnał EKG
średnia ważona
wnioskowanie bayesowskie
ECG signal
weighted averaging
Bayesian inference
Opis:: An electrocardiogram (ECG) is the prime tool in non-invasive cardiac electrophysiology and has a prime function in the screening and diagnosis of cardiovascular diseases. However one of the greatest problems is that usually recording an electrical activity of the heart is performed in the presence of noise. The paper presents empirical Bayesian approach to problem of signal averaging which is commonly used to extract a useful signal distorted by a noise. The averaging is especially useful for biomedical signal such as ECG signal, where the spectra of the signal and noise significantly overlap. In reality the variability of noise can be observed, with power from cycle to cycle, which is motivation for weighted averaging methods usage. It is demonstrated that by exploiting a probabilistic Bayesian learning framework, it can be derived accurate prediction models offering significant additional advantage, namely automatic estimation of 'nuisance' parameters. Performance of the new method is experimentally compared to the traditional averaging by using arithmetic mean and weighted averaging method based on criterion function minimization.
Źródło:: Journal of Medical Informatics & Technologies; 2006, 10; 93-101
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Characteristic points detection in ECG signal using Bayesian learning and fuzzy system
Autorzy:: Momot, M.
Momot, A.
Powiązania:: https://bibliotekanauki.pl/articles/333840.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: sygnał EKG
systemy rozmyte
ECG signal
fuzzy systems
bayesian learning
Opis:: Characteristic points detection such as beginnings and ends of P-wave, T-wave or QRS complex is one of primary aims in automated analysis of ECG signal. The paper presents one possible approach based on Bayesian inference to design of kernel based classifier. The classification function is constructed using the probability distribution function of standard normal distribution and independent Gaussian random variables. The parameters of such variables are computed using iterative Expectation-Maximization algorithm. This approach is used to calculate parameters of classification function to modelling Takagi-Sugeno-Kang fuzzy systems. Numerical experiment of characteristic points detection in ECG signal using CTS database is also presented.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 171-176
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Weighted averaging of ECG signals based on partition of input set in time domain
Autorzy:: Momot, A.
Momot, M.
Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333836.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: sygnał EKG
ważone uśrednianie
redukcja hałasu
ECG signal
weighted averaging
noise reduction
Opis:: The paper presents new approach to problem of signal averaging which is commonly used to extract a useful signal distorted by a noise. The averaging is especially useful for biomedical signal such as ECG signal, where the spectra of the signal and noise significantly overlap. In reality can be observed variability of noise power from cycle to cycle which is motivation for using methods of weighted averaging. Performance of the new method, based on partition of input set in time domain and criterion function minimization, is experimentally compared with the traditional averaging by using arithmetic mean, weighted averaging method based on empirical Bayesian approach and weighted averaging method based on criterion function minimization.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 165-170
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: New frontiers of analysis, interpretation and classification of biomedical signals: a computational intelligence framework
Autorzy:: Gacek, A.
Powiązania:: https://bibliotekanauki.pl/articles/333497.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: sygnał EKG
inteligencja obliczeniowa
zbiory rozmyte
granulki informacji
ziarnista informatyka
interpretacja
klasyfikacja
współdziałanie
ECG signals
computational intelligence
neurocomputing
fuzzy sets
information granules
granular computing
interpretation
classification
interpretability
Opis:: 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.
Źródło:: Journal of Medical Informatics & Technologies; 2011, 17; 23-36
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: An application of the Lp-norm in robust weighted averaging of biomedical signals
Autorzy:: Pander, T.
Przybyła, T.
Czabański, R.
Powiązania:: https://bibliotekanauki.pl/articles/333017.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: robust weighted averaging
Lp-norm
ECG signal
solidna uśredniania ważona
Lp-norma
sygnał EKG
Opis:: Averaging is one of the basic methods of statistical analysis of experimental data where the response of the system is periodic or quasi-periodic. As long as the noise are Gaussian, the standard averaging leads to good results and effective noise reduction. However, when the distortions have impulsive nature, then such an approach leads to a deterioration of the system. In this case the robust methods should be applied which are characterized by resistance to a statistical sample spoken. In this work a robust averaging method based on the minimization of a scalar criterion function using a Lp-norm functions are presented. The effectiveness of the proposed method was tested in an averaging periods aligned ECG signal cycles in the presence of impulse noise.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 71-78
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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