Temat: ekg - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Sequential separation of twin pregnancy electrocardiograms
Autorzy:: Kotas, M.
Leski, J. M.
Wrobel, J.
Powiązania:: https://bibliotekanauki.pl/articles/202099.pdf
Data publikacji:: 2016
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: fetal ECG
twin pregnancy
ECG signals decomposition
blind source separation
independent component analysis
source subspaces
projective filtering
adaptive filtering
EKG płodu
ciąża bliźniacza
EKG
separacja sygnałów niewidomych
niezależna analiza składowych
podprzestrzenie źródła
filtracja adaptacyjna
Opis:: We propose to tackle the problem of maternal abdominal electric signals decomposition with a combined application of independent component analysis and projective or adaptive filtering. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. These signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of various origin. Although the independent component analysis cannot separate the respective signals, the proposed combination of the methods deals with this task successfully. A simulation experiment confirms high efficiency of this approach.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 1; 91-101
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
Autorzy:: Kołodziej, Marcin
Majkowski, Andrzej
Tarnowski, Paweł
Rak, Remigiusz Jan
Rysz, Andrzej
Powiązania:: https://bibliotekanauki.pl/articles/2090741.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: epilepsy
seizure detection
seizure prediction
convolutional neural network
deep learning
ECG
HRV
cardiac sympathetic index
padaczka
wykrywanie napadu
przewidywanie napadu
splotowa sieć neuronowa
głęboka nauka
technika deep learning
EKG
wskaźnik współczulny serca
Opis:: Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136921, 1--9
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
Autorzy:: Kołodziej, Marcin
Majkowski, Andrzej
Tarnowski, Paweł
Rak, Remigiusz Jan
Rysz, Andrzej
Powiązania:: https://bibliotekanauki.pl/articles/2173565.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: epilepsy
seizure detection
seizure prediction
convolutional neural network
deep learning
ECG
HRV
cardiac sympathetic index
padaczka
wykrywanie napadu
przewidywanie napadu
splotowa sieć neuronowa
głęboka nauka
technika deep learning
EKG
wskaźnik współczulny serca
Opis:: Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136921
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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