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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
Tytuł:
Methods of extracting electrocardiograms from electronic signals and images in the Python environment
Autorzy:
Zholmagambetova, Bakhytgul
Mazakov, Talgat
Jomartova, Sholpan
Izat, Adilzhan
Bibalayev, Olzhas
Powiązania:
https://bibliotekanauki.pl/articles/328664.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
ECG signal
MIT/BIH
Python
image processing
one-dimensional array
OpenCV
Matplotlib
NumPy
sygnał EKG
przetwarzanie obrazu
Opis:
High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.
Źródło:
Diagnostyka; 2020, 21, 3; 95-101
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A preliminary study of the utilization of a low resolution ECG signal from handheld ECG monitor
Autorzy:
Kostorz, I.
Kowalski, W.
Ludwig, Z.
Zając, J.
Piasecki, A.
Socha, M.
Górka, W.
Powiązania:
https://bibliotekanauki.pl/articles/332853.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
ECG signal analysis
low resolution signals
handheld monitor
telemedicine
analiza sygnału EKG
sygnały o niskiej rozdzielczości
monitor przenośny
telemedycyna
Opis:
The paper presents the preliminary study of the utilization of a low resolution ECG signal analysis. The analysis was performed on the signals obtained from a hand-held ECG monitor usually used in primary health care. The aim. The main aim of the study was a registration of series of data by volunteers within couple of months and determination of signal quality and main ECG parameters as follows: Q, R, S waves, QRS duration as well as the end of PQ and the beginning of ST segment. Additionally, the heart rate variability was determined. Materials and methods. The data was registered by 12 volunteers aged from 35 to 55. The ECG tests were carried out for 7 months. The sample rate of the signal was 100 Hz. To determine the ECG parameters the signal processing and statistical methods was used. Results. The sensitivity of the following ECG parameters were: R wave detection - 99,2 %, Q wave detection - 99,1 %, S wave detection - 99,0 %, QRS duration - 99,2 % respectively.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 191-198
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Heart work analysis by means of recurrence-based methods
Zastosowanie metody diagramów rekurencyjnych w diagnostyce i klasyfikacji chorób serca
Autorzy:
Iwaniec, J.
Iwaniec, M.
Powiązania:
https://bibliotekanauki.pl/articles/327372.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
ECG signal
heart work
cardiovascular dysfunctions
recurrence plots
RQA analysis
sygnał EKG
cykl pracy serca
zaburzenia rytmu serca
metoda diagramów rekurencyjnych
analiza RQA
Opis:
Currently, for the purposes of recorded ECG signals (electrocardiograms) interpretation, the classical methods involving analysis of geometrical properties of the recorded waveforms in time domain are used. Such an analysis consists in determining the values of parameters describing the heart rate and rhythm. However, these indicators can not be treated as an infallible criterion for diagnosis and, moreover, the limits of increasing the accuracy of ECG analysis by increasing the accuracy of determining its characteristic points have already been reached. Therefore, in the paper, for the purposes of analysis of registered ECG signals and acoustical recordings of heart work, it is proposed to use the recurrence plots and RQA analysis methods that consist in searching for the recurrence properties of the registered signals. Application of the recurrence-based methods is natural due to the cyclic character of the heart work while providing patterns characteristic for different cardiac dysfunctions supported by objective, quantitative measures will contribute to early, credible and reliable classification of cardiovascular dysfunction.
Obecnie, do analizy zarejestrowanych sygnałów EKG, wykorzystywane są metody detekcji punktów charakterystycznych, czyli metody badania własności geometrycznych analizowanych sygnałów w dziedzinie czasu. Jednak wyznaczone parametry opisujące zmienność rytmu serca nie są niezawodnym kryterium rozpoznania choroby. Z tego względu, w artykule, do analizy zarejestrowanych sygnałów EKG zaproponowano łączne zastosowanie metod klasycznych (obecnie stosowanych metod badania własności geometrycznych EKG) oraz metod diagramów rekurencyjnych (RP) i analizy RQA, polegających na badaniu rekurencyjności trajektorii fazowych badanych układów. Zastosowanie metod badania własności rekurencyjnych do analizy sygnałów EKG jest naturalne ze względu na cykliczny charakter pracy serca, natomiast określenie cech dystynktywnych charakterystycznych dla różnych chorób serca przyczynia się do zwiększenia wiarygodności a także niezawodności diagnostyki i klasyfikacji chorób serca.
Źródło:
Diagnostyka; 2017, 18, 4; 89-96
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea
Autorzy:
Kandukuri, Usha Rani
Prakash, Allam Jaya
Patro, Kiran Kumar
Neelapu, Bala Chakravarthy
Tadeusiewicz, Ryszard
Pławiak, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/24200694.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sleep apnea
convolutional neural network
constant Q-transform
deep learning
single lead ECG signal
non apnea
obstructive sleep apnea
bezdech senny
sieć neuronowa konwolucyjna
uczenie głębokie
sygnał EKG
obturacyjny bezdech senny
Opis:
Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 493--506
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Event detection in ECG, carotid pulse, phonocardiogram, and detection of consecutive systolic time intervals
Autorzy:
Strasz, A.
Niewiadomski, W.
Skupińska, M.
Gąsiorowska, A.
Laskowska, D.
Leonarcik, R.
Cybulski, G.
Powiązania:
https://bibliotekanauki.pl/articles/384357.pdf
Data publikacji:
2012
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
systolic time intervals
ECG
polyphysiography
automatic signal analysis
Opis:
We developed a program which allows measurement of consecutive time intervals between chosen events in ECG, carotid pulse, and phonocardiogram. Currently it is possible to determine following systolic time intervals (STI): PEP - pre-ejection period, Q-S2 – time between trough of Q wave and aortic valve closure, Q-D - time between trough of Q wave and dicrotic notch, S2-D - time between aortic valve closure and dicrotic notch, as well as QQ interval. Measurements were performed on 30 young, healthy subjects. Subjects were supine, they performed two-minute isometric handgrip (HG) twice. First HG was followed by four-minute rest, second HG by two-minute occlusion of the working arm. Preliminary analyses revealed: 1/ the QQ interval changes were reflected weakly or not at all in changes of STI, 2/ shortening of QQ during handgrip was paralleled by slight decrease of Q-D and Q-S2, 3/ during occlusion, when QQ intervals returned to baseline also Q-D and Q-S2 returned to baseline, despite sustained elevation of arterial pressure, 4/ there were distinct oscillation in the time course of Q-S2 intervals, time course of Q-D intervals was relatively smooth, thus S2 and D may reflect different events contrary to common notation.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2012, 6, 3; 13-16
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
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