Automated Detection of Depolarization and Repolarization of Cardiac Signal for Arrhythmia Classification

Irregular heartbeat results in heart diseases. Cardiac deaths are most seen across the globe. Detecting the heart problems in early stage can reduce the death rate. Electrocardiogram (ECG) is one of the most popular method for diagnosing different arrhythmias. Arrhythmia means irregular activity of heart or abnormal heart rhythm. In this paper, cardiac signal peaks P-wave, QRS complex and T-wave are detected for classifying the type of arrhythmia. These are the main components of ECG signal. P-wave is of very small duration, it is ex- plains about the atrial depolarization. The QRS complex may include combination of Q-wave, R-wave, and S-wave. But every QRS complex may not contain Q-R-S waves. It explains about ventricular depolarization. Whereas T wave is about ventricular re-polarization. S-Golay filter is used for denoising. This is used for smoothing the data which thereby, increases the precision of data without distortion of signal tendency. The patient data is collected from MIT-BIH Arrhythmia database for analysis. The simulation is done in Matlab software

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Research on the duration of electrocardiogram components in pregnant goats

Medycyna Weterynaryjna ◽

10.21521/mw.6625 ◽

2022 ◽

Vol 78 (03) ◽

pp. 6625-2022

Author(s):

MARIAN GHIȚĂ ◽

IULIANA CODREANU ◽

CARMEN PETCU ◽

ADRIAN RĂDUȚĂ ◽

DRAGOȘ POPESCU ◽

...

Keyword(s):

Medical Practice ◽

Electric Activity ◽

P Wave ◽

Qrs Complex ◽

T Wave ◽

Main Components

The electrocardiogram is a graph recording of heart’s electric activity, so it is used in medical practice mainly in order to observe the heart’s activity. The values of the main components of the electrocardiogram in pregnant goats were determined within the current research. All of these were performed in three different stages of pregnancy (the beginning, the middle and the ending), being focused on the variation of these values during the pregnancy. The gestation diagnosis was confirmed by ultrasound-exam. During the pregnancy, the following values for the duration of the main ECG’s components were found: the P wave (0.045-0.044 s), the P-R segment (0.061-0.048 s), of the P-R range (0.105-0.086 s), of the QRS complex (0.042-0.040 s), of the Q-T range (0.242-0.218 s), of the P-T range (0.377-0.368 s), of the R-R range (0.465-0.431 s), the T wave (0.091-0.104 s) and of the T-P segment (0.097-0.101 s). Our results show that during the pregnancy the duration of: the P wave, the P-R segment, the P-R range, the QRS complex, the Q-T range, the P-T range and the R-R range, decrease, while the duration of the T wave and the T-P segment increase.

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Electrocardiogram Changes with Acute Alcohol Intoxication: A Systematic Review

The Open Cardiovascular Medicine Journal ◽

10.2174/1874192401812010001 ◽

2018 ◽

Vol 12 (1) ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Hitesh Raheja ◽

Vinod Namana ◽

Kirti Chopra ◽

Ankur Sinha ◽

Sushilkumar Satish Gupta ◽

...

Keyword(s):

Cardiac Arrhythmias ◽

Alcohol Intoxication ◽

P Wave ◽

Acute Alcohol Intoxication ◽

Qrs Complex ◽

Qtc Prolongation ◽

Ecg Changes ◽

T Wave ◽

St Segment ◽

Electrocardiogram Ecg

Background: Acute alcohol intoxication has been associated with cardiac arrhythmias but the electrocardiogram (ECG) changes associated with acute alcohol intoxication are not well defined in the literature. Objective: Highlight the best evidence regarding the ECG changes associated with acute alcohol intoxication in otherwise healthy patients and the pathophysiology of the changes. Methods: A literature search was carried out; 4 studies relating to ECG changes with acute alcohol intoxication were included in this review. Results: Of the total 141 patients included in the review, 90 (63.8%) patients had P-wave prolongation, 80 (56%) patients had QTc prolongation, 19 (13.5%) patients developed T-wave abnormalities, 10 (7%) patients had QRS complex prolongation, 3 (2.12%) patients developed ST-segment depressions. Conclusion: The most common ECG changes associated with acute alcohol intoxication are (in decreasing order of frequency) P-wave and QTc prolongation, followed by T-wave abnormalities and QRS complex prolongation. Mostly, these changes are completely reversible.

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Research on the Values of Amplitudes of Electrocardiographic Waves Depending on Age in Kids

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca Veterinary Medicine ◽

10.15835/buasvmcn-vm:2020.0019 ◽

2020 ◽

Vol 77 (2) ◽

pp. 80

Author(s):

Dragos Corneliu COTOR ◽

Gabriel GAJAILA ◽

Aurel DAMIAN ◽

Ana Maria ZAGRAI ◽

Carmen PETCU ◽

...

Keyword(s):

Statistical Analysis ◽

Electric Activity ◽

P Wave ◽

Mechanical Activity ◽

Qrs Complex ◽

T Wave ◽

Electrocardiogram Ecg

The electrocardiogram (ECG) is a graphical recording of the cardiac electric activity during cardiac revolutions. This bio-current triggers and maintains the mechanical activity of the heart. Within this research, the amplitudes values of the electrocardiographic waves were determined in 6 leads: I, II, III, aVL, aVR and aVF. Thus, some electrocardiograms were recorded using limb lead in clinically healthy kids, aged 1 month, 3 months and 5 months, in order to achieve the proposed objectives. Then, the statistical analysis of the obtained results was performed using t (student) test.As a consequence of the interpretation of the obtained results, it was noticed that the limb leads can be used successfully for recording the electrocardiogram in kids because it provides an easy aspect to interpret. The highest amplitude of the electrocardiographic waves is recorded in I lead, in the case of the 1 month old kids (having the following values: 0.115 mV ± 0.010 for P wave; 0.625 mV ± 0.078 for QRS complex; 0.460 mV ± 0.045 for T wave) and in II lead (having the following values for the 3 months old kids: 0.071 mV ± 0.015 for P wave; 0.540 mV ± 0.064 for QRS complex; 0.310 mV ± 0.052 for T wave and having the following values for the 5 months old kids: 0.071 mV ± 0.015 for P wave; 0.455 mV ± 0.028 for QRS complex; 0.430 mV ± 0.026 for T wave). It also found that the lowest amplitude of electrocardiographic waves is recorded in the aVF lead, but this lead cannot be used for ECG recording in kids.

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Electrocardiogram assessment using the Einthoven and base-apex lead systems in healthy Holstein cows and neonates

Pesquisa Veterinária Brasileira ◽

10.1590/s0100-736x2016001300001 ◽

2016 ◽

Vol 36 (suppl 1) ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Dario A. Cedeno ◽

Maria L.G. Lourenço ◽

Carmen A.B. Daza ◽

Plinio Pagnani Filho ◽

Simone B. Chiacchio

Keyword(s):

Heart Rhythm ◽

Perinatal Period ◽

P Wave ◽

Holstein Cows ◽

Qrs Complex ◽

S Wave ◽

Axis Orientation ◽

Significant Difference ◽

Baseline Information ◽

The Difference

Abstract: The objective aimed to describe the electrocardiographic behavior of parameters in Holstein pregnant cows and neonates during the perinatal period. The electrocardiograms were performed using a computerized electrocardiogram. The animals selected for the study were 23 cows and 18 neonates. Maternal electrocardiographic examinations were conducted in the 35, 28, 21, 14, 7 days and one-day pre -partum and the neonates were evaluated in six moments; at the time of birth, 7, 14, 21, 28 and 35 days after delivery. The evaluations were done in pre and post-delivery cows and into the group of neonates between female and male. For each electrocardiographic recording P-wave duration and amplitude, PR interval and the QRS complex duration, R, S-wave amplitude and polarity, QT and RR interval duration were examined. Changes in heart rate, ST segment and T wave polarity were recorded in leads of Einthoven and base-apex planes. The mean electrical axis of the QRS complex was calculated. In cows the results when comparing the two leads system, there are significant changes in the amplitude of the waves P, R, S, and T and the duration of the intervals PR, ST and QRS complex. The difference between primiparous and multiparous dairy cows was in the amplitude of the Twave. It was concluded that the base-apex system is a suitable lead for monitoring heart rhythm in Holstein cows and Einthoven in neonates. During the first month of life, no differences in P, Q, S and T waves, in PR, QRS, and ST intervals and in axis orientation was observed in neonates. There was a significant difference in duration of the QT interval. Among sexes, the difference was in the Q amplitude. This study incorporated the calves and Holstein cows in a single study in search of baseline information regarding the duration and morphology of the ECG parameters. In conclusion, it was proved that, with increasing age, there are changes in ECG components associated with variations in the distance between the recording electrode and the heart. The study contributes by providing Holstein reference values for clinical evaluations.

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Characteristics of the reindeer electrocardiogram

Rangifer ◽

10.7557/2.2.2.412 ◽

1982 ◽

Vol 2 (2) ◽

pp. 36

Author(s):

Jouni Timisjärvi ◽

Mauri Nieminen ◽

Sven Nikander

Keyword(s):

Heart Rate ◽

Indirect Evidence ◽

Frontal Plane ◽

Heart Beat ◽

P Wave ◽

Qrs Complex ◽

Single Plane ◽

T Wave ◽

Wide Range ◽

Electrocardiogram Ecg

The electrocardiogram (ECG) provides reliable information about heart rate, initiation of heart beat and also, to some degree, indirect evidence on the functional state of the heart muscle. A wide range of such information is readily obtainable from conventional scalar leads, even if the records are limited to a single plane. The present investigation deals with the normal reindeer ECG in the frontal plane. The technique used is the scalar recording technique based on the Einthovenian postulates. The P wave was positive in leads II, III and aVF, negative in lead aVL and variable in leads I and aVR. The direction of the P vector was 60 to 120°. The QRS complex was variable. The most common forms of QRS complex were R and rS in leads I and aVR; R, Rs and rS in lead aVL and Qr or qR in other leads. The most common direction of the QRS vector was 240 to 300°. The T wave was variable. The duration of various intervals and deflection depended on heart rate.Elektrokardiogram på ren.Abstract in Swedish / Sammandrag: Elektrokardiogramet (EKG) ger tillförlitliga uppgifter om hjärtfrekvens, retledning och, indirekt, delvis även om hjärtmuskelns funktionell tillstånd. Största delen av denna information fås med normal skalar koppling även om registrering sker i ett plan. I detta arbete har renens normala EKG i frontalplanet undersökts. Kopplingarna har baserats på Einthovs postulat. P-vågen var riktad uppåt i koppling II, III och aVF, nedåt i koppling aVL och den varierade i koppling I och aVR. P-vektorns riktning var 60 - 120°. QRS-komplexet varierade. De vanligaste formerna var R och rS i koppling I och aVR; R, Rs och rS i koppling aVL och Qr eller qR i andra kopplingar. Vanligen var QRS-vektorns riktning 240 - 300°. T-vågen varierade. Awikelserna och intervallernas längd var beroende av hiärtfrekvenssen.Poron sydänsähkökäyrän ominaisuuksia.Abstract in Finnish / Yhteenveto: Sydänsähkökäyrästä saadaan luotettavaa tietoa sydämen syketiheydestä, sähköisestä johtumisesta ja välillisesti jossain määrin myös sydänlihaksen toiminnallisesta tilasta. Suurin osa tämänkaltaista tietoa voidaan saada tavanomaisia skalaarisia kytkentöjäkäyttäen, ja usein yhdessä tasossa tapahtuva rekisteröinti on riittävä. Tässä työssä on tutkittu porojen normaalia sydänsähkökäyrää ja sen eri poikkeamien suuntautumista frontaalitasossa, kun rekisteröinnissä on käytetty Einthovenin postulaattien mukaisia raajakytkentöjä. P aalto suuntautui ylöspäin kythkennöissä II, III ja aVF, alaspäin kytkennässä aVL ja vaihteli kytkennöissä I ja aVR. P vektorin suunta oli 60 - 120°. QRS kompleksi vaihteli. Tavallisimmat muodot olivat R ja rS kytkennöissä I ja aVR; R, Rs ja rS kytkennässä aVL ja Qr tai qR muissa kytkennöissä. Tavallisin QRS vektorin suunta oli 240 - 300°. T aalto vaihteli. Poikkeaminen ja intervallien kesto riippui sydämen syketiheydestä.

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Opening the black box: interpretability of machine learning algorithms in electrocardiography

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2020.0253 ◽

2021 ◽

Vol 379 (2212) ◽

Cited By ~ 1

Author(s):

Matteo Bodini ◽

Massimo W. Rivolta ◽

Roberto Sassi

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

P Wave ◽

Qrs Complex ◽

Ecg Signals ◽

T Wave ◽

Cardiac Abnormalities ◽

Challenges And Opportunities ◽

Clinically Significant ◽

Electrocardiogram Ecg

Recent studies have suggested that cardiac abnormalities can be detected from the electrocardiogram (ECG) using deep machine learning (DL) models. However, most DL algorithms lack interpretability, since they do not provide any justification for their decisions. In this study, we designed two new frameworks to interpret the classification results of DL algorithms trained for 12-lead ECG classification. The frameworks allow us to highlight not only the ECG samples that contributed most to the classification, but also which between the P-wave, QRS complex and T-wave, hereafter simply called ‘waves’, were the most relevant for the diagnosis. The frameworks were designed to be compatible with any DL model, including the ones already trained. The frameworks were tested on a selected Deep Neural Network, trained on a publicly available dataset, to automatically classify 24 cardiac abnormalities from 12-lead ECG signals. Experimental results showed that the frameworks were able to detect the most relevant ECG waves contributing to the classification. Often the network relied on portions of the ECG which are also considered by cardiologists to detect the same cardiac abnormalities, but this was not always the case. In conclusion, the proposed frameworks may unveil whether the network relies on features which are clinically significant for the detection of cardiac abnormalities from 12-lead ECG signals, thus increasing the trust in the DL models. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3127 ◽

2020 ◽

Vol 6 (3) ◽

pp. 493-496

Author(s):

Claudia Nagel ◽

Nicolas Pilia ◽

Axel Loewe ◽

Olaf Dössel

Keyword(s):

Healthy Subjects ◽

Large Scale ◽

Machine Learning Algorithms ◽

P Wave ◽

Qrs Complex ◽

T Wave ◽

Healthy Cohort ◽

Electrocardiogram Ecg ◽

Morphology Changes

AbstractThe morphology of the electrocardiogram (ECG) varies among different healthy subjects due to anatomical and structural reasons, such as for example the shape of the heart geometry or the position and size of surrounding organs in the torso. Knowledge about these ECG morphology changes could be used to parameterize electrophysiological simulations of the human heart. In this work, we detected the boundaries of ECG waveforms, i.e. the P-wave, the QRS-complex and the T-wave, in 12- lead ECGs from 918 healthy subjects in the Physionet Computing in Cardiology Challenge 2020 Database with the IBT openECG toolbox. Subsequently, we obtained the onset, the peak and the offset of each P-wave, QRS-complex and T-wave in the signal. In this way, the duration of the P-wave, the QRScomplex and the T-wave, the PQ-, RR- and the QT-interval as well as the amplitudes of the P-wave, the Q-, R- and Speak and the T-wave in each lead were extracted from the 918 healthy ECGs. Their statistical distributions and correlation between each other were assessed. The highest variabilities among the 918 healthy subject were found for the RR interval and the amplitudes of the QRScomplex. The highest correlation was observed for feature pairs that represent the same feature in different leads. Especially the R-peak amplitudes showed a strong correlation across different leads. The calculated feature distributions can be used to optimize the parameters of populations of cardiac electrophysiological models. In this way, realistic in-silico generated surface ECGs can be simulated in large scale and could be used as input data for machine learning algorithms for a classification of cardiovascular diseases.

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Identification of myocardial infarction from analysis of ECG signal

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200043 ◽

2020 ◽

Vol 24 (3) ◽

pp. 217-226

Author(s):

D.B.V. Jagannadham ◽

D.V. Sai Narayana ◽

P. Ganesh ◽

D. Koteswar

Keyword(s):

Myocardial Infarction ◽

Feature Extraction ◽

Early Stage ◽

Heart Diseases ◽

Ecg Signal ◽

St Segment Elevation ◽

T Wave ◽

European Database ◽

St Segment ◽

Electrocardiogram Ecg

Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

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Analysis of Back Propagation Neural Network Method for Heart Disease Recognition

International Journal of Organizational and Collective Intelligence ◽

10.4018/ijoci.2019040104 ◽

2019 ◽

Vol 9 (2) ◽

pp. 45-54

Author(s):

Amit K. Gupta ◽

Ajay Agarwal ◽

Ruchi Rani Garg

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Wavelet Transform ◽

Bp Neural Network ◽

Heart Diseases ◽

Recognition Rate ◽

P Wave ◽

Experimental Result ◽

Qrs Complex ◽

T Wave

ECG is the recording of the electrical activity of the heart, and has become one of the most important tools in the diagnosis of heart diseases. ECG signal is shaped by P wave, QRS complex, and T wave. In the normal ECG beat, the main parameters including shape, duration, R-R interval and relationship between P wave, QRS complex, and T wave components are inspected. Any change in these parameters indicates an illness of the heart. This article introduces an electrocardiogram (ECG) pattern recognition method based on wavelet transform and standard BP neural network classifier. Experiment analyzes wavelet transform of ECG to extract the maximum wavelet coefficients of multi-scale firstly. This article then inputs them into BP to classify for different kinds of ECGs. The experimental result shows that the standard BP neural network classifier's overall pattern recognition rate is well.

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Correlation between ECG changes and early left ventricular remodeling in preadolescent footballers

Physiology International ◽

10.1556/2060.104.2017.1.2 ◽

2017 ◽

Vol 104 (1) ◽

pp. 42-51

Author(s):

M Zdravkovic ◽

B Milovanovic ◽

S Hinic ◽

I Soldatovic ◽

T Durmic ◽

...

Keyword(s):

Ventricular Remodeling ◽

Early Stage ◽

Left Ventricular Remodeling ◽

Left Ventricular ◽

P Wave ◽

Qtc Interval ◽

Interval Duration ◽

S Wave ◽

Ecg Changes ◽

T Wave

The aim of this study was to assess the early electrocardiogram (ECG) changes induced by physical training in preadolescent elite footballers. This study included 94 preadolescent highly trained male footballers (FG) competing in Serbian Football League (minimum of 7 training hours/week) and 47 age-matched healthy male controls (less than 2 training hours/week) (CG). They were screened by ECG and echocardiography at a tertiary referral cardio center. Sokolow–Lyon index was used as a voltage electrocardiographic criterion for left ventricular hypertrophy diagnosis. Characteristic ECG intervals and voltage were compared and reference range was given for preadolescent footballers. Highly significant differences between FG and CG were registered in all ECG parameters: P-wave voltage (p < 0.001), S-wave (V1 or V2 lead) voltage (p < 0.001), R-wave (V5 and V6 lead) voltage (p < 0.001), ECG sum of S V1–2 + R V5–6 (p < 0.001), T-wave voltage (p < 0.001), QRS complex duration (p < 0.001), T-wave duration (p < 0.001), QTc interval duration (p < 0.001), and R/T ratio (p < 0.001). No differences were found in PQ interval duration between these two groups (p > 0.05). During 6-year follow-up period, there was no adverse cardiac event in these footballers. None of them expressed pathological ECG changes. Benign ECG changes are presented in the early stage of athlete’s heart remodeling, but they are not related to pathological ECG changes and they should be regarded as ECG pattern of LV remodeling.

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