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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
Artykuł
Tytuł:
Comparison of methods for correcting outliers in ECG-based biometric identification
Autorzy:
Jun, Su
Szmajda, Miroslaw
Khoma, Volodymyr
Khoma, Yuriy
Sabodashko, Dmytro
Kochan, Orest
Wang, Jinfei
Powiązania:
https://bibliotekanauki.pl/articles/221531.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Euclidean distance
autoencoders
outlier correction
ECG signal
human identification
biometrics
Opis:
The aim of this paper is to compare the efficiency of various outlier correction methods for ECG signal processing in biometric applications. The main idea is to correct anomalies in various segments of ECG waveform rather than skipping a corrupted ECG heartbeat in order to achieve better statistics. Experiments were performed using a self-collected Lviv Biometric Dataset. This database contains over 1400 records for 95 unique persons. The baseline identification accuracy without any correction is around 86%. After applying the outlier correction the results were improved up to 98% for autoencoder based algorithms and up to 97.1% for sliding Euclidean window. Adding outlier correction stage in the biometric identification process results in increased processing time (up to 20%), however, it is not critical in the most use-cases.
Źródło:
Metrology and Measurement Systems; 2020, 27, 3; 387-398
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of Obstructive Sleep Apnea from ECG Signal Using SVM Based Grid Search
Autorzy:
Valavan, K. K.
Manoj, S.
Abishek, S.
Gokull Vijay, T. G.
Vojaswwin, P.
Rolant Gini, J.
Ramachandran, K. I.
Powiązania:
https://bibliotekanauki.pl/articles/1844601.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
ECG signal
grid search
RR interval
sleep apnea
support vector machine
Opis:
Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 1; 5-12
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
R peak determination using a WDFR algorithm and Adaptive threshold
Autorzy:
Nguyen, Thanh-Nghia
Nguyen, Thanh-Hai
Ngo, Ba-Viet
Powiązania:
https://bibliotekanauki.pl/articles/38437166.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
ECG signal
wavelet transforms
WDFR algorithm
R peak determination
adaptive threshold
Opis:
The determination of the R peak position in the ECG signal helps physicians not only to know the heart rate per minute, but also to monitor the patient’s health related to heart disease. This paper proposes a system to accurately determine the R peak position in the ECG signal. The system consists of a pre-processing block for filtering out noise using a WDFR algorithm and highlighting the amplitude of the R peak and a threshold value is calculated for determining the R peak. In this research, the MIT-BIH ECG dataset with 48 records are used for evaluation of the system. The results of the SEN, +P, DER and ACC parameters related to the system quality are 99.70%, 99.59%, 0.70% and 99.31%, respectively. The obtained performance of the proposed R peak position determination system is very high and can be applied to determine the R peak of the ECG signal measuring devices in practice.
Źródło:
Applied Computer Science; 2022, 18, 3; 19-30
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
Congestive heart failure detection based on Electrocardiomatrix Method with ECG Signal
Autorzy:
Rao, B. Mohan
Kumar, Aman
Powiązania:
https://bibliotekanauki.pl/articles/38703034.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
electrocardiomatrix
ECG signal
congestive heart failure
elektrokardiomatrix
sygnał EKG
zastoinowa niewydolność serca
Opis:
Congestive heart failure (CHF) is a prevalent, expensive to treat, and dangerous disease inwhich the pumping capacity of the heart muscle is reduced due to injury or stress. It causesmajor medical problems in humans and contribute to many diseases, thus increasing themortality rate. In a world with a growing population, there is a need for more precise andsimpler approaches to detect such conditions, which can prevent many diseases and lead toa lower mortality rate. The main goal here is to use electrocardiomatrix (ECM) approachto perform the task of detecting CHF. It is detected quickly and accurately with thisapproach, as ECM converts 2D electrocardiogram (ECG) data into a 3D-colored matrix.The approach is tested using ECG readings from the Beth Israel Deaconess Medical Center(BIDMC) CHF Database on the Internet (Physionet.org). The ECM outcomes of are thencompared to manual readings of ECG data. The ECM results achieved the accuracy of96.89%, the sensitivity of 97.53%, the precision of 99.1%, the F1-score of 97.76%, and thespecificity of 96.02% for CHF. This research shows that the ECM approach is a good wayfor machines and practitioners to interpret long-term ECG readings while maintainingaccuracy.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 291-304
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Application of Statistical Features and Multilayer Neural Network to Automatic Diagnosis of Arrhythmia by ECG Signals
Autorzy:
Slama, A. B.
Lentka, Ł.
Mouelhi, A.
Diouani, M. F.
Sayadi, M.
Smulko, J.
Powiązania:
https://bibliotekanauki.pl/articles/221289.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Multilayer Neural Network
arrhythmia diagnosis
ECG signal processing
Principal Component Analysis
Fisher’s Linear Discriminant
Opis:
Abnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregularity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated by additive noise components. This paper explores a method of de-noising ECG signal by the discrete wavelet transform (DWT) and further detecting arrhythmia by estimated statistical parameters. Parameters of the de-noised ECG signals were used to form an input data vector determining whether the examined patient suffers from a cardiac arrhythmia or not. Input data were transformed using selected linear methods in order to reduce dimension of the input vector. A neural network was used to detect illness. Compared with the results of recent studies, the proposed method provides more accurate diagnosis based on the examined ECG signal data.
Źródło:
Metrology and Measurement Systems; 2018, 25, 1; 87-101
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
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