Wyniki wyszukiwania - Biblioteka Nauki

1

Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

100%

Malik K. G. , Kumar Y. , Panda M.

Annals of Computer Science and Information Systems

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2017

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tom Vol. 10

241--244

EN

Purpose of this work is to develop an automated physiological signal diagnostic tool that can help us to early determination of arrhythmia for proper medical attention. This paper presents a simple automated approach for classification of normal and abnormal ECG based on arrhythmia. The proposed method validated by the data MIT BIH arrhythmia database. The performance in terms of accuracy for clinical decision must be very high. This method uses fourth order wavelet decomposition, wavelet decomposition used for time frequency representation and feature extraction. For classification support vector machine is used for detection kinds of ECG signals

2

Komputerowy system do monitorowania analizy i archiwizacji sygnałów EKG

100%

Zygarlicki J. , Muciek A.

Zeszyty Naukowe. Elektryka / Politechnika Opolska

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2004

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tom Vol. 297, z. 54

5-30

PL

W artykule przedstawiono konstrukcję i zasadę pracy komputerowego systemu pomiarowego, przeznaczonego do rejestracji i analizy sygnałów EKG. System ten składa się z przystawki, do której podłączone są elektrody pomiarowe, komputera klasy PC oraz oprogramowania. Opisano algorytmy kondycjonowania i analizy sygnału elektrokardiograficznego realizowane w części programowej systemu. Zaproponowano nową metodę wygładzania krzywej EKG opartą na aproksymacji fragmentów sygnału parabolą. Przedstawiono algorytm usuwania offsetu sygnału, algorytm realizacji filtrów cyfrowych, metody wykrywania załamków QRS oraz metodę obliczenia parametrów zmienności rytmu serca HRV. W końcowej części pracy podano wyniki badań systemu.

EN

A simple computer measuring system designed for monitoring and analysing of ECG signals is presented in the paper. The system consists of a personal computer and an accessory unit to which measurement probes are connected. Method of ECG signal, conditioning and analysis are described. In particular: a novel method of ECG signal smoothing based on approximation of the signal segments by a parabola, design and numerical realization of digital filters, algorithm of curves recovering and a method of HRV parameters assessment. Results of ECG signal analysis obtained from the system are presented.

3

Investigations of real conditions properties of the biomedical electrodes with integrated electrode contact quality monitoring system for recording electrocardiographic signals

84%

Marszalek Z. , Gawedzki W.

Przegląd Elektrotechniczny

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2016

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tom R. 92, nr 11

55--58

EN

The results of investigations of the metrological properties of the system of 4 biomedical electrodes with the continuous control of the contact quality, are presented in the hereby paper. Investigations were performed under real conditions of measuring the electrocardiographic signals of humans, at the application of the typical ECG instrumentation. Due to the performed experiments the conformity of the electrocardiographic signals obtained by means of the standard gel electrodes glued to the body and the ones obtained by means of the electrodes with continuous control of the contact quality, was confirmed.

PL

W artykule przedstawiono wyniki badań właściwości metrologicznych układu 4 elektrod do pomiaru sygnałów elektrokardiograficznych człowieka z ciągłą kontrolą stanu kontaktu, przy wykorzystaniu typowej aparatury EKG. Potwierdzono zgodność zapisów sygnałów EKG wykonanych za pomocą standardowych elektrod żelowych przyklejanych do ciała oraz przy zastosowaniu elektrod z ciągłą kontrolą stanu kontaktu.

4

Comparison and evaluation of LMS-derived algorithms applied on ECG signals contaminated with motion artifact during physical activities

84%

Galdos J. , Lopez N. , Medina A. , Huarca J. , Rendulich J. , Sulla E.

Applied Computer Science

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2024

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tom Vol. 20, no 1

157--172

EN

The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

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New frontiers of analysis, interpretation and classification of biomedical signals: a computational intelligence framework

59%

Gacek A.

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tom Vol. 17

23--36

EN

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.