Cardiac Arrhythmias Auto Detection in an Electrocardiogram Using Computer-Aided Diagnosis Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 2728-2731
Author(s):  
Ji Ae Park ◽  
Seok Min Hwang ◽  
Ji Won Baek ◽  
Yoon Nyun Kim ◽  
Jong Ha Lee
Keyword(s):  
Heart Disease ◽  
Cardiac Arrhythmias ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Great Accuracy ◽  
Normal Sinus ◽  
Computer Aided ◽  
Diagnosis Algorithm ◽  
Aided Diagnosis ◽  
Healthy Persons

Supraventricular tachycardia (SVT) is the most common arrhythmia and can be found in not only heart disease patients, but also healthy persons. However, the occurrence of SVT in heart disease patients implies that the potential of the heart diseases worsening, and it causes cardiac arrest when it evolves into ventricular tachycardia or the ventricular fibrillation. Therefore, the detection of SVT arrhythmia, as a first stage, has significant implications for the prevention of cardiac arrests. In this paper, we propose the automatic diagnosis system for cardiac arrhythmias detection with great accuracy. To validate the algorithm, SVT and normal sinus rhythm are classified by the proposed algorithm.

scholarly journals Multiscale Entropy Technique Discriminates Single Lead ECG’s With Normal Sinus Rhythm and Sleep Apnea

2018 ◽  
Author(s):  
Suganti Shivaram ◽  
Anjani Muthyala ◽  
Zahara Z. Meghji ◽  
Susan Karki ◽  
Shivaram Poigai Arunachalam
Keyword(s):  
Sleep Apnea ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Multiscale Entropy ◽  
Ischemic Heart Diseases ◽  
Normal Sinus ◽  
Airway Obstructions ◽  
Entropy Technique ◽  
Leg Movements

Sleep apnea is characterized by abnormal interruptions in breathing during sleep due to partial or complete airway obstructions affecting middle-aged men and women on an estimated ∼4% of the population [1]. While the disorder is clinically manageable to relieve patients, the challenge occurs with diagnosis, with many patients going undiagnosed leading to further complications such as ischemic heart diseases, stroke etc. Sleep apnea also significantly affects the quality of day to day life causing sleepiness and fatigue. Polysomnography (PSG) technique is currently a used for detecting sleep apnea which is a comprehensive sleep test to diagnose sleep disorders by recording brain waves, the oxygen level in the blood, heart rate, breathing, eye and leg movements during the study. However, PSG test is very expensive, requires patients to stay overnight and is known to cause inconvenience to the patients.

scholarly journals Directed networks as a novel way to describe and analyze cardiac excitation: Directed Graph mapping

2019 ◽  
Author(s):  
Nele Vandersickel ◽  
Enid Van Nieuwenhuyse ◽  
Nico Van Cleemput ◽  
Jan Goedgebeur ◽  
Milad El Haddad ◽  
...  
Keyword(s):  
Cardiac Arrhythmias ◽  
Normal Sinus Rhythm ◽  
Social Systems ◽  
Daily Practice ◽  
Directed Network ◽  
Directed Networks ◽  
Normal Sinus ◽  
Graph Mapping ◽  
Brain Data ◽  
Cardiac Excitation

AbstractNetworks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proofof-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow to determine the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood.

Some effects of breath holding and apneic underwater diving on cardiac rhythm in man

1962 ◽  
Vol 17 (3) ◽  
pp. 461-466 ◽  
Author(s):  
C. Robert Olsen ◽  
Darrell D. Fanestil ◽  
Per F. Scholander
Keyword(s):  
Cardiac Arrhythmias ◽  
Cardiac Rhythm ◽  
Sinus Tachycardia ◽  
Normal Sinus Rhythm ◽  
Sinus Bradycardia ◽  
Breath Holding ◽  
Sinus Arrest ◽  
Sinus Arrhythmia ◽  
Normal Sinus ◽  
Idioventricular Rhythm

Man's bradycardic response to simple breath holding was augmented by submersion in water of 27 C and was not prevented by muscular exercise. Cardiac arrhythmias occurred with 45 of 64 periods of apnea in 16 subjects and were more frequent during the dives than during breath holding. These arrhythmias, with the exception of atrial, nodal, and ventricular premature contractions, were inhibitory in type and included sinus bradycardia and arrhythmia, sinus arrest followed by either nodal escape or ventricular escape, A-V block, A-V nodal rhythm, and idioventricular rhythm. T waves frequently became tall and peaked during both breath holding and dives. Prompt return to normal sinus rhythm was the rule with the first breath after surfacing. Sinus tachycardia, sinus arrhythmia, and atrial, nodal, or ventricular premature contractions were seen during recovery. Submitted on October 9, 1961

scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

Congenital Atrial Flutter: Report of a Case Documented by Intra-uterine Electrocardiogram

PEDIATRICS ◽  
1968 ◽  
Vol 41 (3) ◽  
pp. 659-661
Author(s):  
Sidney Blumenthal ◽  
Jerry C. Jacobs ◽  
Charles M. Steer ◽  
Susan W. Williamson
Keyword(s):  
Heart Disease ◽  
Sinus Rhythm ◽  
Atrial Flutter ◽  
Newborn Infant ◽  
Normal Sinus Rhythm ◽  
Newborn Infants ◽  
In Utero ◽  
Good Prognosis ◽  
Normal Sinus

A newborn infant shown to have atrial flutter in utero and after delivery was successfully converted to normal sinus rhythm with intramuscular digoxin. He remains well at 2 years of age. This is the first patient to be reported in whom atrial flutter was demonstrated by intra-uterine electrocardiography. This arrhythmia, when present in newborn infants without other signs of heart disease, has a good prognosis.

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