Researches Regarding the Values of Some Electrocardiogram’S Components in Cats

The electrocardiogram (ECG or EKG) represents the graphical recording of the cardiac electrical activity (Ghiţă et al., 2005) and it is useful in the diagnosis in some cardiac diseases (such as rhythm disorders) (Cotor and Ghiţă, 2014) or frequency disorders (Ghiţă et al., 2007).The main target of this research work was to determine the values of the main components of the ECG and the cardiac frequency. The biological material was represented by 12 healthy cats of different breeds. The values obtained in this research work can be used as reference values in ECG interpretation in cats.

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Comparative Review of the Algorithms for Removal of Electrocardiographic Interference from Trunk Electromyography

Sensors ◽

10.3390/s20174890 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4890

Author(s):

Lin Xu ◽

Elisabetta Peri ◽

Rik Vullings ◽

Chiara Rabotti ◽

Johannes P. Van Dijk ◽

...

Keyword(s):

Electrical Activity ◽

Biceps Brachii ◽

Signal To Noise Ratio ◽

Surface Emg ◽

Cardiac Electrical Activity ◽

Emg Signal ◽

Comparative Review ◽

Electrocardiogram Ecg ◽

High Pass

Surface electromyogram (EMG) is a noninvasive measure of muscle electrical activity and has been widely used in a variety of applications. When recorded from the trunk, surface EMG can be contaminated by the cardiac electrical activity, i.e., the electrocardiogram (ECG). ECG may distort the desired EMG signal, complicating the extraction of reliable information from the trunk EMG. Several methods are available for ECG removal from the trunk EMG, but a comparative assessment of the performance of these methods is lacking, limiting the possibility of selecting a suitable method for specific applications. The aim of the present study is therefore to review and compare the performance of different ECG removal methods from the trunk EMG. To this end, a synthetic dataset was generated by combining in vivo EMG signals recorded on the biceps brachii and healthy or dysrhythmia ECG data from the Physionet database with a predefined signal-to-noise ratio. Gating, high-pass filtering, template subtraction, wavelet transform, adaptive filtering, and blind source separation were implemented for ECG removal. A robust measure of Kurtosis, i.e., KR2 and two EMG features, the average rectified value (ARV), and mean frequency (MF), were then calculated from the processed EMG signals and compared with the EMG before mixing. Our results indicate template subtraction to produce the lowest root mean square error in both ARV and MF, providing useful insight for the selection of a suitable ECG removal method.

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ECG Interpretation: Clinical Relevance, Challenges, and Advances

Hearts ◽

10.3390/hearts2040039 ◽

2021 ◽

Vol 2 (4) ◽

pp. 505-513

Author(s):

Nikita Rafie ◽

Anthony H. Kashou ◽

Peter A. Noseworthy

Keyword(s):

Cost Effective ◽

Clinical Workflow ◽

Non Invasive ◽

Electrolyte Disturbances ◽

Cardiac Electrical Activity ◽

Technological Advances ◽

Electrocardiogram Ecg ◽

Potential Use ◽

Tremendous Impact ◽

Ecg Interpretation

Since its inception, the electrocardiogram (ECG) has been an essential tool in medicine. The ECG is more than a mere tracing of cardiac electrical activity; it can detect and diagnose various pathologies including arrhythmias, pericardial and myocardial disease, electrolyte disturbances, and pulmonary disease. The ECG is a simple, non-invasive, rapid, and cost-effective diagnostic tool in medicine; however, its clinical utility relies on the accuracy of its interpretation. Computer ECG analysis has become so widespread and relied upon that ECG literacy among clinicians is waning. With recent technological advances, the application of artificial intelligence-augmented ECG (AI-ECG) algorithms has demonstrated the potential to risk stratify, diagnose, and even interpret ECGs—all of which can have a tremendous impact on patient care and clinical workflow. In this review, we examine (i) the utility and importance of the ECG in clinical practice, (ii) the accuracy and limitations of current ECG interpretation methods, (iii) existing challenges in ECG education, and (iv) the potential use of AI-ECG algorithms for comprehensive ECG interpretation.

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The Role of Computer Modeling in Electrocardiography

Methods of Information in Medicine ◽

10.1055/s-0038-1634809 ◽

1990 ◽

Vol 29 (04) ◽

pp. 282-288 ◽

Cited By ~ 2

Author(s):

A. van Oosterom

Keyword(s):

Electrical Activity ◽

Computer Modeling ◽

Body Surface ◽

Electrical Current ◽

The Body ◽

Volume Conductor ◽

Current Generator ◽

Cardiac Electrical Activity ◽

Source Models

AbstractThis paper introduces some levels at which the computer has been incorporated in the research into the basis of electrocardiography. The emphasis lies on the modeling of the heart as an electrical current generator and of the properties of the body as a volume conductor, both playing a major role in the shaping of the electrocardiographic waveforms recorded at the body surface. It is claimed that the Forward-Problem of electrocardiography is no longer a problem. Several source models of cardiac electrical activity are considered, one of which can be directly interpreted in terms of the underlying electrophysiology (the depolarization sequence of the ventricles). The importance of using tailored rather than textbook geometry in inverse procedures is stressed.

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A guide to modelling cardiac electrical activity in anatomically detailed ventricles

Progress in Biophysics and Molecular Biology ◽

10.1016/j.pbiomolbio.2007.07.004 ◽

2008 ◽

Vol 96 (1-3) ◽

pp. 19-43 ◽

Cited By ~ 120

Author(s):

R.H. Clayton ◽

A.V. Panfilov

Keyword(s):

Electrical Activity ◽

Cardiac Electrical Activity

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ECG Holter analysis to detect induced deconditioning on cardiac activity in bedridden volunteers

European Heart Journal ◽

10.1093/ehjci/ehaa946.3444 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

S Solbiati ◽

A Paglialonga ◽

L Costantini ◽

E.G Caiani

Keyword(s):

Electrical Activity ◽

Cardiac Activity ◽

Bed Rest ◽

Funding Source ◽

Healthy Male ◽

Tukey Test ◽

T Wave ◽

Rehabilitation Programs ◽

Cardiac Electrical Activity ◽

Ethical Approval

Abstract Introduction Prolonged bed rest (BR) is an unnatural state, often related to hospitalization, chronic diseases and ageing, inducing reduced functional capacity in multiple body systems, possibly leading to cardiovascular deconditioning. We hypothesized that measuring this decline over time could represent the first step for the formulation of appropriate countermeasures or rehabilitation programs while in the hospital. Accordingly, our aim was to assess the effects of 10-day horizontal BR on cardiac electrical activity. Methods Ten healthy male volunteers (23±5 years) were enrolled in an hospital, after ethical approval and signed consent, to participate to a 10-day strict horizontal BR campaign, preceded and followed by 2 days in the facility, respectively as acclimatization and recovery. The 12-leads 24-hours Holter ECG (1000 Hz, H12+, Mortara Instrument Inc.) was acquired 1 day before BR (PRE), the 5th (BR5) and 10th day (BR10) of bedridden immobilization. From each recording, beat-to-beat RR and QTend interval series, as well as T wave amplitude (Tamp) and upslope (Tslope) were computed. Statistical analysis was applied to test changes induced by BR (ANOVA with Tukey test, p<0.05), separately for day (7:00–23:00) and night (23:00–7:00) periods. Results Daily RR and QTend duration increased during BR, with peak changes at BR5 compared to PRE (+13.3% and +3% respectively), and were still prolonged at BR10 (+12.6% and +2.6%). During the night, while RR increased (BR5:+5.3%; BR10:+1.3%), QTend was found progressively shortened (BR5: −1.6%; BR10: −2.9%). Also, day and night Tamp (BR10: −19.5%) and Tslope (BR10 day: −17.1%; night: −7.8%) were found progressively reduced with the duration of BR. Conclusion During BR, cardiac electrical activity is affected by 10-days bedridden immobilization. Noticeably, a mismatch in RR-QTend relation was visible at night, where vagal autonomic system activity is prevailing. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Agenzia Spaziale Italiana (ASI)

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Imaging 3-dimensional Cardiac Electrical Activity from Intra-Cavity Potentials

2006 International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2006.259561 ◽

2006 ◽

Cited By ~ 2

Author(s):

B. He

Keyword(s):

Electrical Activity ◽

3 Dimensional ◽

Cardiac Electrical Activity

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In-vivo evaluation of reduced-lead-systems in noninvasive reconstruction and localization of cardiac electrical activity

2015 Computing in Cardiology Conference (CinC) ◽

10.1109/cic.2015.7408626 ◽

2015 ◽

Author(s):

Matthijs JM Cluitmans ◽

Joel Karel ◽

Pietro Bonizzi ◽

Monique MJ de Jong ◽

Paul GA Volders ◽

...

Keyword(s):

Electrical Activity ◽

In Vivo Evaluation ◽

Cardiac Electrical Activity

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Embedded Programmable Web-based ECG Monitoring & Detection System Using a Fast Algorithm

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2016.1355 ◽

2016 ◽

pp. 141-145

Author(s):

K. Rajamohan ◽

K.Hanumantha Rao ◽

T. Malyadri

Keyword(s):

Critical Care ◽

Electrical Activity ◽

Fast Algorithm ◽

Human Error ◽

Detection System ◽

Ecg Monitoring ◽

Web Based ◽

Ecg Interpretation ◽

Ecg Data

The physicians have to interpret this large amount of ECG data to search for only a few abnormal beats in the ECG. Physicians may overlook some abnormal cycles due to fatigue and human error in interpreting such a large amount of data. Therefore, there is an urgent need for an automatic ECG interpreting system to help to reduce the burden of ECG interpretation. This proposed system is expected to monitor the electrical activity of heart of the patient under critical care more conveniently and accurately for diagnosing.

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