Results 21 to 30 of about 64,157 (326)

The prevalence of wide QRS complex (≥110 ms) among the population, depending on sex, age and place of residence

open access: yesРоссийский кардиологический журнал, 2020
Aim. To assess the prevalence of wide QRS complex (≥110 ms) among the population, depending on sex, age, place of residence (urban or rural area), the presence of obesity and cardiovascular disease.Material and methods. The analysis was based on the ESSE-
G. A. Muromtseva   +21 more
doaj   +1 more source

Novel electrocardiographic criteria for the diagnosis of arrhythmogenic right ventricular cardiomyopathy [PDF]

open access: yes, 2015
Aims: In order to improve the electrocardiographic (ECG) diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC), we evaluated novel quantitative parameters of the QRS complex and the value of bipolar chest leads (CF leads) computed from the ...
Bastiaenen, Rachel   +6 more
core   +1 more source

Wide QRS complex and the risk of major arrhythmic events in Brugada syndrome patients: A systematic review and meta‐analysis

open access: yesJournal of Arrhythmia, 2020
Background Brugada syndrome (BrS) is an inherited arrhythmic disease associated with an increased risk of major arrhythmic events (MAE). Previous studies reported that a wide QRS complex may be useful as a predictor of MAE in BrS patients.
Pattara Rattanawong   +9 more
doaj   +1 more source

PERANAN DURASI QRS DAN SKOR QRS SELVESTER DALAM KEBERHASILAN REPERFUSI MIOKARD

open access: yesMajalah Kedokteran Andalas, 2015
AbstrakPerubahan gambaran elektrokardiogram (EKG) terjadi pada fase akut IMA EST baik berupa perubahan repolarisasi ataupun perubahan depolarisasi. Skor QRS Selvester dan pemanjangan kompleks QRS merupakan parameter yang digunakan untuk memperkirakan ...
Yose Ramda Ilhami
doaj   +1 more source

ECG filtering and QRS extraction under steep pulse interference

open access: yes工程科学学报, 2020
Applying a steep pulse voltage of appropriate amplitude to a cell membrane can induce transient and reversible breakdown of the membrane, which has broad application prospects in biomedicine and clinical fields.
Xi-tong YAO   +5 more
doaj   +1 more source

An evaluation of planarity of the spatial QRS loop by three dimensional vectorcardiography: its emergence and loss [PDF]

open access: yes, 2017
Aims: To objectively characterize and mathematically justify the observation that vectorcardiographic QRS loops in normal individuals are more planar than those from patients with ST elevation myocardial infarction (STEMI).
Goswami, Damodar Prasad   +4 more
core   +1 more source

VLSI Implementation of QRS Complex Detector Based on Wavelet Decomposition

open access: yesIEEE Access, 2022
This paper presents a very large–scale integration chip for a novel discrete wavelet transform (DWT) based QRS complex detection algorithm. In many aspects of electrocardiogram (ECG) analyses, QRS complex detection is the first step.
Yuan-Ho Chen   +5 more
doaj   +1 more source

Design of a web laboratory interface for ECG signal analysis using MATLAB builder NE

open access: yesOpen Computer Science, 2022
An electrocardiogram (ECG) is a noninvasive test, determining any defect in the heart rate or rhythm or changes in the shape of the QRS complex is very significant to detect cardiac arrhythmia.
Jaber Hussain A.   +2 more
doaj   +1 more source

Detection of multi-class arrhythmia using heuristic and deep neural network on edge device

open access: yesIJAIN (International Journal of Advances in Intelligent Informatics), 2023
Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based
Arief Kurniawan   +5 more
doaj   +1 more source

Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM

open access: yesIEEE Access, 2020
In this paper, we propose a novel machine learning pipeline to detect QRS complexes in very noisy wearable electrocardiogram (ECG) devices. The machine learning pipeline consists of a Butterworth filter, two wavelet convolutional neural networks ...
Brosnan Yuen, Xiaodai Dong, Tao Lu
doaj   +1 more source

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