Temat: sygnał - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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: 2.

Tytuł:: Comparing methods of ECG respiration signals derivation based on measuring the amplitude of QRS complexes
Autorzy:: Kikta, A.
Augustyniak, P.
Powiązania:: https://bibliotekanauki.pl/articles/333821.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: EKG
sygnał oddechowy
wykrywanie bezdechu
ECG
EDR
respiratory signal
apnea detection
Opis:: This paper presents the study of algorithms for derivation of respiration waveform from the electrocardiogram. The problem has considerable clinical impact, because the heart rate and respiration are both driven by the central nervous system, and commonly used low-cost Holter recording may be used for efficient detection of breath disturbances (e.g. apnea). Three methods based on: heart rate, heart position and lung resistance influencing the ECG amplitude were compared in our research. Among 18 volunteers breathing at a controlled frequency all implemented algorithms show acceptable sensitivity of order of 97% in slow breathing phases detection. In fast breathing the sensitivity is reduced to 90%, since the heart beats are too sparse with regard of respiration waveform.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 155-163
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

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: 4.

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: 5.

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: 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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Skocz do pozycji: 7.

Tytuł:: Fuzzy clustering based methods for nystagmus movements detection in electronystagmography signal
Autorzy:: Czabański, R.
Pander, T.
Horoba, K.
Przybyła, T.
Powiązania:: https://bibliotekanauki.pl/articles/332952.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: saccade detection
optokinetic nystagmus
fuzzy clustering
detekcja ruchów sakadowych
oczopląs optokinetyczny
grupowanie rozmyte
sygnał ENG
Opis:: The analysis of optokinetic nystagmus (OKN) provides valuable information about the condition of human vision system. One of the phenomena that is used in the medical diagnosis is optokinetic nystagmus. Nystagmus are voluntary or involuntarily eye movements being a response to a stimuli which activate the optokinetic systems. The electronystagmography (ENG) signal corresponding to the nystagmus has a form of a saw tooth waveform with fast components related to saccades. The accurate detection of the saccades in the ENG signal is the base for the further estimation of the nystagmus characteristic. The proposed algorithm is based on the proper filtering of the ENG signal providing a waveform with amplitude peaks corresponding the fast eyes rotation. The correct recognition of the local maxima of the signal is obtained by the means of fuzzy c-means clustering (FCM). The paper presents three variants of saccades detection algorithm based on the FCM. The performance of the procedures was investigated using the artificial as well as the real optokinetic nystagmus cycles. The proposed method provides high detection sensitivity and allows for the automatic and precise determination of the saccades location in the preprocessed ENG signal.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 277-283
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Automated detection of fetal movements in Doppler ultrasound signals versus maternal perception
Autorzy:: Wróbel, J.
Kupka, T.
Horoba, K.
Matonia, A.
Roj, D.
Jeżewski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333732.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fetal movement activity
Doppler ultrasound signal
fetal heart rate
aktywność ruchowa płodu
sygnał USG Dopplera
tętno płodu
Opis:: Analysis of movement activity is important since it enables detection of nonreactive fetal heart rate recordings. The aim of the study was to develop an algorithm for automated detection of the fetal movement activity (actogram), based on analysis of the Doppler ultrasound signal, and to evaluate a reliability of the actogram as a source of information about the fetal movements. Bandpass filtering (20-80 Hz) was used to separate the actogram signal describing the fetal movement activity. Simultaneously there were recorded the markers of fetal movements perceived by mother, being the reference information. For the determination of the binary actogram, the authors proposed an algorithm in which the classification threshold was estimated at the beginning of each recording and was adaptively modified during its duration. The algorithm ensured detection of up to 89% of movement episodes corresponding to movements perceived by mother. At the same time almost as high number of episodes not related to the reference information was recognized. Obtained results revealed that the automated analysis of fetal movements is characterized by much higher sensitivity of movement episode detection compared to the maternal perception.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 43-50
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Reconstruction of FHR series recorded via ultrasound - method validation using abdominal fetal electrocardiography
Autorzy:: Kupka, T.
Horoba, K.
Roj, D.
Matonia, A.
Czabanski, R.
Jezewski, M.
Powiązania:: https://bibliotekanauki.pl/articles/333726.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fetal heart rate variability
time event series
sampled signal
duplicated sample
zmienność rytmu serca płodu
sygnał próbny
powielona próbka
Opis:: Analysis of variability of the fetal heart rate (FHR) is very important for fetal wellbeing assessment. The beat-to-beat variability is described quantitatively by the indices originated from invasive fetal electrocardiography which provides the FHR signal in a form of time event series. Nowadays, monitoring instrumentation is based on Doppler ultrasound technology. The fetal monitors provide the output signal in a form of evenly spaced measurements. The goal of this work is to present a new method for the FHR signal processing, which enables extraction of time series of consecutive heartbeat intervals from the evenly repeated values. The proposed correction algorithm enables recognition and removal of the duplicated measurements. Reliable evaluation of the algorithm requires the reference event series, thus the FHR signals were obtained from abdominal fetal electrocardiograms to be used in this research study.
Źródło:: Journal of Medical Informatics & Technologies; 2013, 22; 135-141
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

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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