scholarly journals Effect ageing on atrial premature beat and its consecutiveness.The study according to Holter electrocardiography.

1991 ◽  
Vol 11 (1) ◽  
pp. 53-57
Author(s):  
TOMOAKI SAEKI
Keyword(s):  
Atrial Premature Beat

scholarly journals The transition of the tachycardia from narrow to wide by a spontaneous atrial premature beat: What is the mechanism?

2021 ◽  
Author(s):  
Meryem Kara ◽  
Ahmet Korkmaz ◽  
Bulent Deveci ◽  
Tolga Cimen ◽  
Ozcan Ozeke ◽  
...  
Keyword(s):  
Atrial Premature Beat

FRACTAL ANALYSIS OF THE ELECTROCARDIOGRAM SIGNAL

2014 ◽  
Vol 14 (04) ◽  
pp. 1450055 ◽  
Author(s):  
IBTICEME SEDJELMACI ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Premature Ventricular Contraction ◽  
Multifractal Spectrum ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
Linear Approach ◽  
Atrial Premature Beat ◽  
The Subject ◽  
Electrocardiogram Signal ◽  
Electrocardiogram Ecg

In this paper, the analysis of the electrocardiogram (ECG) signal is carried out according a non-linear approach. This concerns the eventual fractal behavior of such signal and the correlation of such behavior with normal and pathological ECG signals. The analysis is carried out on different ECG signals taken from the MIT-BIH arrhythmia database. In fact these signals are those of six subjects with different ages and presenting both normal and abnormal arrhythmias situations. The abnormal situations are atrial premature beat (APB), premature ventricular contraction (PVC), right bundle branch block (RBBB) and left bundle branch block (LBBB). The fractal behavior of these signals is analyzed according to the determination of the multifractal spectrum and the fractal dimension variations and looking for eventually a fractal signature of each heart disease and age of the subject. The obtained results show a fractal signature according to the age and the pathologies for the studied cases. However further investigations are required on larger databases to confirm such results.

scholarly journals Automatic ECG Diagnosis Using Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 951 ◽  
Author(s):  
Roberta Avanzato ◽  
Francesco Beritelli
Keyword(s):  
Confusion Matrix ◽  
Premature Ventricular Contraction ◽  
Disease Diagnosis ◽  
Ecg Signals ◽  
Testing Dataset ◽  
Life Threatening ◽  
Causes Of Mortality ◽  
Atrial Premature Beat ◽  
Heart Disease Diagnosis ◽  
Electrocardiogram Ecg

Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

scholarly journals Heart rate turbulence can be detected after atrial premature beat

2002 ◽  
Vol 39 ◽  
pp. 100
Author(s):  
Irina Savelieva ◽  
Dan Wichterle ◽  
Azad Ghuran ◽  
Maggie Meara ◽  
John Camm ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Turbulence ◽  
Atrial Premature Beat

Response of LBBB tachycardia to an atrial premature beat: What is the mechanism?

2017 ◽  
Vol 40 (7) ◽  
pp. 892-893
Author(s):  
Krishna Kumar Mohanan Nair ◽  
Narayanan Namboodiri ◽  
Amitabh Poonia ◽  
Sreevilasam Pushpangadhan Abhilash ◽  
Ajitkumar Valaparambil
Keyword(s):  
Atrial Premature Beat

scholarly journals Deep Learning-Based Arrhythmia Detection in Electrocardiograph

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yang Meng ◽  
Guoxin Liang ◽  
Mei Yue
Keyword(s):  
Heart Beat ◽  
Small Sample ◽  
Original Model ◽  
Massachusetts Institute Of Technology ◽  
Beth Israel Hospital ◽  
Arrhythmia Detection ◽  
Atrial Premature Beat ◽  
The Difference ◽  
Institute Of Technology ◽  
Sample Set

This study aimed to explore the application of electrocardiograph (ECG) in the diagnosis of arrhythmia based on the deep convolutional neural network (DCNN). ECG was classified and recognized with the DCNN. The specificity (Spe), sensitivity (Sen), accuracy (Acc), and area under curve (AUC) of the DCNN were evaluated in the Chinese Cardiovascular Disease Database (CCDD) and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, respectively. The results showed that in the CCDD, the original model tested by the small sample set had an accuracy (Acc) of 82.78% and AUC of 0.882, while the Acc and AUC of the translated model were 85.69% and 0.893, respectively, so the difference was notable ( P  < 0.05); the Acc of the original model and the translated model was 80.12% and 82.63%, respectively, in the large sample set, so the difference was obvious ( P  < 0.05). In the MIT-BIH database, the Acc of normal (N) heart beat (HB) (99.38%) was higher than that of the atrial premature beat (APB) (87.45%) ( P  < 0.05). In a word, applying the DCNN could improve the Acc of ECG for classification and recognition, so it could be well applied to ECG signal classification.

scholarly journals Electrical Alternans

2015 ◽  
pp. 65-6
Author(s):  
Yoga Yuniadi
Keyword(s):  
Atrial Premature Beat ◽  
Electrical Alternans

Seorang perempuan, 50 tahun datang ke poli Aritmia dengan keluhan berdebar. Rekaman EKG 12-sadapan diperlihatkan pada gambar di bawah ini:Irama dasar rekaman EKG di atas adalah irama sinus yang dibuktikan dengan morfologi gelombang P yang berasal dari lokasi nodus sinoatrial yaitu negatif di aVR dan positif di sadapan II, III dan aVF. Perhatikan di sadapan aVF pada denyutan 1 interval PR memanjang hingga 280 mdet menunjukkan suatu blok AV derajat 1. Yang menarik adalah denyutan ke 2 dan ke 4 merupakan suatu atrial premature beat dengan morfologi QRS yang berbeda dari morfologi QRS pada denyutan 1. Sebaliknya denyutan ke 3 dan ke 5 yang merupakan denyut sinus pun memiliki kompleks QRS yang berlainan. Terlihat jelas bahwa perubahan morfologi QRS itu hanya tampak pada sadapan ekstremitas tetapi kurang terlihat pada sadapan prekordial.

scholarly journals Using Deep Learning Detection Of Arrhythmia

2019 ◽  
Vol 27 ◽  
pp. 03001
Author(s):  
Zhang Yue ◽  
Li Feng
Keyword(s):  
Deep Learning ◽  
Large Error ◽  
Arrhythmia Detection ◽  
Single Output ◽  
Average Accuracy ◽  
Multiple Input ◽  
Atrial Premature Beat ◽  
Point Detection ◽  
Deep Learning Model

The field of deep learning applications is becoming more widespread. The use of traditional algorithms for arrhythmia detection is cumbersome and the algorithm complexity is relatively high. Using the deep learning model to directly input data into the model will make it difficult to effectively segment the data, which will have a large error in the recognition accuracy. We collected data from six commonly used ECG databases and then used the label shuffling method to amplify the samples, effectively overcoming the effects of sample imbalance. The LSTM model is used for feature point detection to effectively locate the key feature points of the ECG signal, thereby completing the segmentation and processing of the data. Finally, the use of a multiple input model single output for arrhythmia detection has achieved significant results. The average accuracy of the final arrhythmia classification of the model reached 0.711. The accuracy of detecting the fusion of ventricular and normal beat and the aberrated atrial premature beat exceeds 0.9.

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