A review of electrocardiogram filtering

Luo, Shen; Johnston, Paul

doi:10.1016/j.jelectrocard.2010.07.007

Cited by 165 publications

(85 citation statements)

References 11 publications

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“…The ElectroCardioGramme (ECG) is the most commonly performed examination because it is quick to put in place, inexpensive and above all non-invasive and therefore very inconvenient for the patient [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

Patil¹,

Singh²,

Thakare³

et al. 2018

IJET

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This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classification is done for automatic detection of heart disease using Support Vector Machine (SVM) and Cuckoo Search Optimized Neural Network. ECG beats with 4 types of abnormalities (RBBB, APC, PVC and LBBB) from ECG records is retrieved from the MIT-BIH arrhythmia database. Analysis of the different groups shows the overall recognition performance was 99.50%. The worst is 99.63% for the RBBB class.

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Section: Introductionmentioning

confidence: 99%

Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

Patil¹,

Singh²,

Thakare³

et al. 2018

IJET

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“…Schrope et al (1995) reported a significant effect of R wave amplitudes when frequency filters were used in cats. Furthermore, Luo and Johnston (2010) published that T-P segment changes could be caused by a high-pass filter when a linear phase at higher cut-off (0.67 Hz) is used and when the heart rate is very low.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiographic reference values for healthy Netherland Dwarf rabbits and the influence of body position, age and gender

et al. 2017

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The aim of this study was to provide reference values for a single, popular breed of pet rabbit. Moreover, additional objectives were to determine whether sex, body position or age alter Netherland Dwarf rabbit electrocardiographic variables and whether the use of electrocardiographic filters affects those variables. Forty Netherland Dwarf rabbits were examined clinically and standard six-lead electrocardiograms (ECGs) were recorded in sternal and then dorsal recumbency. At first power-line and anti-drift filters were used and then they were disabled. The following variables were measured in lead II: heart rate; P wave duration and amplitude; P-R interval; QRS duration; R wave amplitude (with and without filters); Q-T interval; T wave duration and amplitude; S-T segment; J-T duration; and mean electrical axis (MEA) (with and without filters). MEA was determined by 3 different methods. After statistical processing of the data, our results showed that there were no significant differences between both recumbencies, with the exception of the J-T duration, which was higher in dorsal recumbency. The R wave amplitude using electrocardiographic filters showed significant differences between males (0.083 mV) and females (0.115 mV; P<0.05); and between younger rabbits (0.108 mV) and older rabbits (0.097 mV; P<0.05). These differences were not shown between R waves with filters disabled. Moreover, the strongest correlation was between 2 MEA methods without filters. MEA was more leftward in the pet rabbit than in other species (dog or cats). In conclusion, electrocardiography recording without electrocardiographic filters should be assessed when it is possible, and the specific ECGs characteristics for Netherland Dwarf rabbit should be taken into account.

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“…In order to mimic real noisy recordings and to prove different preprocessing methods, clean ECGs are usually corrupted with usual types of noise, like baseline wander (BW), muscle artifact (MA), electrode motion (EM) or 50-60Hz powerline interference and with different magnitudes of them [3].…”

Section: Introductionmentioning

confidence: 99%

Clinical Severity of Noise in ECG

Everss

Melgarejo-Meseguer

Gimeno-Blanes

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Objective noise metrics such as the signal to noise ratio and the root mean squared value, are not always related to the actual impact that noise can have on the clinical evaluation of cardiac signals. This work is intended to design a database along with a set of criteria to be used as an initial solid gold-standard of noise severity within a scale created from the clinical point of view. Different from previous approaches, we used recordings from external cardiac event recorders, which have a signal morphology and characteristics typical for the usual Holter monitoring devices. These recorded events often correspond to noise segments, which makes simpler the retrieval of a variety of signal segments with real noise examples. We generated a database recordings from 8 patients (4 female and 4 male with a clinical indication for long-term monitoring, 64.75±25.44 years old). In a first attempt, a set of rules for clinical quality recording were initially defined by a cardiologist, so segments with clear clinical description were categorized into free, low, moderate, hard, or other noise. Segments with unclear description are set apart for revision. This process was iterated until convergence of expert opinion and labels. A specific software tool was created and modified for supporting the process for each iteration. The relative duration of the different kinds of noise in both leads according to their clinical severity was: 4h 14m 15s (free, 37.32%), 2h 54m 52s (moderated, 25.67%), 2h 21m 46s (hard, 20.81%), 1h 43m 13s (low, 15.15%) and 07m 07s -other-(1.05%). The generated database and criteria represent a valuable gold standard for clinical quality evaluation of noise impact on ECG signals.

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A review of electrocardiogram filtering

Cited by 165 publications

References 11 publications

Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

Electrocardiographic reference values for healthy Netherland Dwarf rabbits and the influence of body position, age and gender

Clinical Severity of Noise in ECG

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