Wyniki wyszukiwania - Biblioteka Nauki

1

Analiza arytmii serca psow badanych w latach 1996-2001 w Klinice Chorob Koni, Psow i Kotow Wydzialu Medycyny Weterynaryjnej AR we Wroclawiu

100%

Paslawska U. , Noszczyk-Nowak A. , Okreglicka E. , Nicpon J.

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nr 11

1191-1195

2

SpEC: A system for patient specific ECG beat classification using deep residual network

100%

Allam J. P. , Samantray S. , Ari S.

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tom Vol. 40, no. 4

1446--1457

EN

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen) = 98.84%, Specificity (Spe) = 99.50%, Positive predictivity (Ppr) = 98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image = 0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.

3

Czestoskurcz komorowy u psa i kota

100%

Kurski B.

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tom 12

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nr 10

9-11

4

Andersen-Tawil syndrome – a case report

100%

Wójcik-Borowska K. , Chrościńska-Krawczyk M. , Zienkiewicz E. , Małka M. , Klatka M.

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nr 54

71-73

PL

Zespół Andersen-Tawil (Andersen-Tawil syndrome – ATS, long QT syndrome type 7, LQTS Type 7) jest rzadko występującą kanałopatią potasową, spowodowaną głównie mutacją genu KCNJ2. Część przypadków spowodowana jest mutacją genu KCNJ5. Mutacja dziedziczona autosomalnie dominująco jest wykrywana u około 60% pacjentów z rozpoznanym zespołem. Około 30% mutacji może być spowodowanych de novo. Obraz kliniczny obejmuje triadę objawów: okresowe, napadowe osłabienie siły mięśniowej występujące po dłuższym spoczynku lub w trakcie spoczynku po intensywnym wysiłku fizycznym, wydłużenie odstępu QT wraz z zaburzeniami rytmu serca i cechy dysmorficzne (m.in. niski wzrost, hypoteloryzm, hipoplazja żuchwy, krótkie palce rąk i stóp). Duża zmienność fenotypowa oraz często subtelne objawy kliniczne powodują trudności diagnostyczne. Jednak wczesne rozpoznanie jest kluczowe ze względu na możliwości zapobiegania i leczenia objawów neurologicznych oraz zaburzeń rytmu serca, które mogą być przyczyną nagłego zgonu. Celem pracy jest przedstawienie przypadku 15 letniego pacjenta z zespołem Andersen-Tawil.

EN

Andersen-Tawil syndrome is a rare potassium ion channelopathy. Mutations in KCNJ2 are the primary cause. The second causative gene is KCNJ5. Mutations inherited as an autosomal dominant characteristic, are usually found in 60% of patients with the diagnosed syndrome. De novo mutations occur in 30% of cases. The clinical presentation of the syndrome includes: periodic, paroxysmal muscular weakness occurring after prolonged rest or during rest after prolonged physical exertion, prolonged QT interval, cardiac arrhythmias and dysmorphic features (short stature, hypertelorism, hypoplastic mandible, short fingers and toes). Phenotypic heterogeneity and subtle physical symptoms cause diagnostic difficulties. However, early diagnosis is crucial because of the possibility of prevention and treatment of neurological symptoms and cardiac arrhythmias, which can be the cause of sudden death. The aim of this paper is to present the case report of a 15-year-old patient with Andersen-Tawil syndrome.

5

Spectral entropy and deep convolutional neural network for ECG beat classification

100%

Asgharzadeh-Bonab A. , Amirani M. C. , Mehri A.

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2020

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tom Vol. 40, no. 2

691--700

EN

Sudden cardiac death is the result of abnormal heart conditions. Therefore, early detection of such abnormal conditions is vital to identify heart problems. Hence, in this paper, we aim to present a new computer-aided diagnosis (CAD) method based on time-frequency analysis of electrocardiogram (ECG) signals and deep neural networks for arrhythmia detection. Time-frequency transforms have the capability of providing spectral information at different times, which is very useful for analyzing non-stationary signals. On the other side, entropy is an attractive measurement from ECG signals which can distinguish different types of them. In this paper, time-frequency spectral entropy is proposed to extract the efficient features from ECG signals. All computed entropies cannot provide separability among different classes, two-directional two-dimensional principal component analysis (2D2PCA) can be used to reduce the dimension of the extracted features. Finally, the convolutional neural network (CNN) classifies the time-frequency features to diagnose the ECG beat signals and detect arrhythmias. The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

6

Comparison of T-wave alternans detection methods

100%

Janusek D. , Pawłowski Z. , Karczmarewicz S. , Przybylski A.

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tom Vol. 24, no. 4

31-41

EN

T-wave alternans (TWA), which has recently been detected at the micro-volt level, is a non-invasive marker of the vulnerability to ventricular arrhythmia. The aim of this study was to compare the presently used TWA detection methods and to determine the ECG signal acquisition and processing conditions ensuring the TWA detection at its lowest possible level. The results of the evaluation and comparison of seven methods are presented. The ECG signal analysis was performed in the time and frequency domain. The differential and correlation methods were applied as the time domain methods. The complex demodulation method and methods based on the FFT and Karhunen-Loeve transform were tested as the frequency domain methods. The T-wave alternans markers were measured in 28 patients, and the results were compared. The usefulness of TWA detection methods has been demonstrated to dependant on the signal properties, such as the nonstationarity of TWA and the level of noise. Strong correlation between the magnitude of the T-wave alternans and ventricular repolarization time is described. A new, spectral methods is proposed.

7

Cardiac arrhythmia alarm from optical interferometric signals during resting or sleeping for early intervention

80%

Cikajlo I. , Šprager S. , Erjavec T. , Zazula D.

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tom Vol. 36, no. 1

267--275

EN

Diagnostics of cardiac arrhythmias and frequent interventions may contribute to early detection of diseases or even prevent sudden death. Generally electrocardiograph with several on body electrodes at outpatient clinic is applied and the procedure requires a medical expert. We propose cardiac arrhythmia estimation on the basis of heartbeat detection with optical fibers integrated in the bedding. The modified Michelson's interferometer with error detection was used to measure and maximum a-posteriori probability was used to estimate the beat-to-beat intervals. The consistency of heartbeat intervals was examined with simultaneous measurement with clinical electrocardiograph in 10 healthy volunteers and 10 patients with diagnosed heart arrhythmias. Heart beat interval data obtained in patients were examined and irregularities/arrhythmias were identified from the medical guidelines. The current system enables assessment also in home environment without any on-body sensor placement or required assistance. Thus early intervention is possible as the irregularities are submitted to the nurse on duty and stored in the database for subsequent more detailed analysis.

8

A review of shockable arrhythmia detection of ECG signals using machine and deep learning techniques

80%

Kavya L. , Karuna Y. , Saritha S. , Prakash A. J. , Patro K. K. , Sahoo S. P. , Tadeusiewicz R. , Pławiak P.

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tom Vol. 34, no. 3

485--511

EN

An electrocardiogram (ECG) is an essential medical tool for analyzing the functioning of the heart. An arrhythmia is a deviation in the shape of the ECG signal from the normal sinus rhythm. Long-term arrhythmias are the primary sources of cardiac disorders. Shockable arrhythmias, a type of life-threatening arrhythmia in cardiac patients, are characterized by disorganized or chaotic electrical activity in the heart’s lower chambers (ventricles), disrupting blood flow throughout the body. This condition may lead to sudden cardiac arrest in most patients. Therefore, detecting and classifying shockable arrhythmias is crucial for prompt defibrillation. In this work, various machine and deep learning algorithms from the literature are analyzed and summarized, which is helpful in automatic classification of shockable arrhythmias. Additionally, the advantages of these methods are compared with existing traditional unsupervised methods. The importance of digital signal processing techniques based on feature extraction, feature selection, and optimization is also discussed at various stages. Finally, available databases, the performance of automated algorithms, limitations, and the scope for future research are analyzed. This review encourages researchers’ interest in this challenging topic and provides a broad overview of its latest developments.

9

Wieści weterynaryjne

80%

Salek A.

Konie i Rumaki

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2018

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nr 10

10

EKG pozwoli zdiagnozowac arytmie

60%

Lewicka A.

Top Agrar Polska

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2003

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nr 06

127