Echocardiography and cardiac arrhythmias

Papadopoulos, Constantinos; Oikonomidis, Dimitrios; Lazaris, Efstathios; Nihoyannopoulos, Petros

doi:10.1016/j.hjc.2017.11.017

Cited by 32 publications

(24 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cardiac arrhythmias refer to irregular heart rhythms, representing abnormal cardiac electrical activities associated with abnormal initiation and conduction of excitation waves in the heart (1). Cardiovascular diseases in association with cardiac arrhythmias can cause heart failure, stroke, or sudden cardiac death (2).…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Zhang

2021

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

Introduction: Electrocardiograms (ECG) provide information about the electrical activity of the heart, which is useful for diagnosing abnormal cardiac functions such as arrhythmias. Recently, several algorithms based on advanced structures of neural networks have been proposed for auto-detecting cardiac arrhythmias, but their performance still needs to be further improved. This study aimed to develop an auto-detection algorithm, which extracts valid features from 12-lead ECG for classifying multiple types of cardiac states.Method: The proposed algorithm consists of the following components: (i) a preprocessing component that utilizes the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and (ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory model.Result: The developed algorithm was trained and tested on ECG data of nine types of cardiac states, fulfilling a task of multi-label classification. It achieved an averaged F1-score and area under the curve at 0.908 and 0.974, respectively.Conclusion: The frame blocking and bidirectional long-short term memory model represented an improved algorithm compared with others in the literature for auto-detecting and classifying multi-types of cardiac abnormalities.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Zhang

2021

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, ECG is of crucial importance for analyzing and identifying various arrhythmias, which reflect the degree of myocardial damage, the corresponding development process, and the functional structure of both atria and ventricles [3,12]. A few research scholars have related medical evidence of ECG toward arrhythmias with the latest experimental study, see [3,4,12,23,29], and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Gao¹

2020

Practical Applications of Electrocardiogram

View full text Add to dashboard Cite

In this chapter, we investigate the most recent automatic detecting algorithms on abnormal electrocardiogram (ECG) in a variety of cardiac arrhythmias. We present typical examples of a medical case study and technical applications related to diagnosing ECG, which include (i) a recently patented data classifier on the basis of deep learning model, (ii) a deep neural network scheme to diagnose variable types of arrhythmia through wearable ECG monitoring devices, and (iii) implementation of the health cloud platform, which consists of automatic detection, data mining, and classifying via the Android terminal module. Our work establishes a cross-area study, which relates artificial intelligence (AI), deep learning, cloud computing on huge amount of data to minishape ECG monitoring devices, and portable interaction platforms. Experimental results display the technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.

show abstract

“…Multi-modality imaging has a diagnostic and prognostic role in patients with cardiac arrhythmias [ 7 , 8 , 9 , 10 ]. A comprehensive echocardiographic examination remains the cornerstone imaging modality to evaluate a patient with cardiac arrhythmias, by detecting or ruling out structural heart disease, evaluating cardiac function, and offering implications for overall prognosis [ 7 , 8 , 9 , 10 ]. Nowadays, speckle tracking echocardiography has a significant role in detecting subclinical left ventricular (LV) systolic dysfunction, in assessing atrial function and has even a prognostic role, in patients with atrial arrhythmias [ 7 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive echocardiographic examination remains the cornerstone imaging modality to evaluate a patient with cardiac arrhythmias, by detecting or ruling out structural heart disease, evaluating cardiac function, and offering implications for overall prognosis [ 7 , 8 , 9 , 10 ]. Nowadays, speckle tracking echocardiography has a significant role in detecting subclinical left ventricular (LV) systolic dysfunction, in assessing atrial function and has even a prognostic role, in patients with atrial arrhythmias [ 7 , 9 ]. Left atrial (LA) longitudinal peak systolic strain has recently been recognised as a non-invasive parameter that can assess the global atrial function, contraction and relaxation, in patients with atrial arrhythmias [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, speckle tracking echocardiography has a significant role in detecting subclinical left ventricular (LV) systolic dysfunction, in assessing atrial function and has even a prognostic role, in patients with atrial arrhythmias [ 7 , 9 ]. Left atrial (LA) longitudinal peak systolic strain has recently been recognised as a non-invasive parameter that can assess the global atrial function, contraction and relaxation, in patients with atrial arrhythmias [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Left Atrial Strain Identifies Increased Atrial Ectopy in Patients with Beta-Thalassemia Major

et al. 2020

View full text Add to dashboard Cite

Patients with beta-thalassemia major (β-ΤΜ) may develop cardiac arrhythmias through a multifactorial mechanism. The current study evaluated the association of cardiac structure and function on echocardiography with atrial ectopic burden on 24-hour tape recording in β-ΤΜ patients. This prospective study included consecutive β-ΤΜ patients. Demographic, laboratory, echocardiographic, cardiac magnetic resonance (CMR) T2* and 24-hour tape recording data were prospectively collected. The patients were classified according to the median value of premature atrial contractions (PACs) on 24-hour tape. In total, 50 β-TM patients (37.6 ± 9.1 years old, 50% male) were divided in 2 groups; PACs ≤ 24/day and > 24/day. Patients with PACs > 24/day were treated with blood transfusion for a longer period of time (39.0 ± 8.6 vs. 32.0 ± 8.9 years, p < 0.007), compared to their counterparts. Older age (OR: 1.121, 95% CI: 1.032–1.217, p = 0.007), longer duration of blood transfusion (OR:1.101, 95% CI:1.019–1.188, p = 0.014), larger LV end-diastolic diameter (OR: 4.522, 95% CI:1.009–20.280, p = 0.049), higher values of LA peak systolic strain (OR: 0.869, 95% CI: 0.783–0.964, p = 0.008), higher MV E/E′ average (OR: 1.407, 95% CI: 1.028–1.926, p = 0.033) and higher right ventricular systolic pressure (OR: 1.147, 95% CI: 1.039–1.266, p = 0.006) were univariably associated with PACs > 24/day. LA peak systolic strain remained significantly associated with PACs > 24/day after adjusting for the duration of blood transfusions or for CMR T2*. The multivariable model including blood transfusion duration and LA peak systolic strain was the most closely associated with PACs > 24/day. Receiver operating characteristic curve analysis identified a left atrial peak systolic strain of 31.5%, as the best cut-off value (83% sensitivity, 68% specificity) for prediction of PACs > 24/day. In β-TM patients, LA peak systolic strain was associated with the atrial arrhythmia burden independently to the duration of blood transfusions and CMR T2*.

show abstract

Echocardiography and cardiac arrhythmias

Cited by 32 publications

References 70 publications

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning

Left Atrial Strain Identifies Increased Atrial Ectopy in Patients with Beta-Thalassemia Major

Contact Info

Product

Resources

About