scholarly journals Application of Wavelet Entropy to Predict Atrial Fibrillation Progression from the Surface ECG

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Raúl Alcaraz ◽  
José J. Rieta
Keyword(s):  
Atrial Fibrillation ◽  
Structural Changes ◽  
Supraventricular Arrhythmia ◽  
Wavelet Entropy ◽  
Non Invasive ◽  
Promising Tool ◽  
Surface Ecg ◽  
Disorder Degree ◽  
Sensitivity Specificity ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory(f)waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

scholarly journals Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Syed Khairul Bashar ◽  
Dong Han ◽  
Shirin Hajeb-Mohammadalipour ◽  
Eric Ding ◽  
Cody Whitcomb ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
False Alarms ◽  
Time Frequency ◽  
Non Invasive ◽  
Accelerometer Signal ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Sensitivity Specificity

Abstract Detection of atrial fibrillation (AF) from a wrist watch photoplethysmogram (PPG) signal is important because the wrist watch form factor enables long term continuous monitoring of arrhythmia in an easy and non-invasive manner. We have developed a novel method not only to detect AF from a smart wrist watch PPG signal, but also to determine whether the recorded PPG signal is corrupted by motion artifacts or not. We detect motion and noise artifacts based on the accelerometer signal and variable frequency complex demodulation based time-frequency analysis of the PPG signal. After that, we use the root mean square of successive differences and sample entropy, calculated from the beat-to-beat intervals of the PPG signal, to distinguish AF from normal rhythm. We then use a premature atrial contraction detection algorithm to have more accurate AF identification and to reduce false alarms. Two separate datasets have been used in this study to test the efficacy of the proposed method, which shows a combined sensitivity, specificity and accuracy of 98.18%, 97.43% and 97.54% across the datasets.

scholarly journals Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6036
Author(s):  
Vincenzo Randazzo ◽  
Jacopo Ferretti ◽  
Eros Pasero
Keyword(s):  
Atrial Fibrillation ◽  
Low Cost ◽  
Portable Devices ◽  
Cardiac Health ◽  
Non Invasive ◽  
Key Factor ◽  
User Friendly ◽  
Electrocardiogram Ecg

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

scholarly journals Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3570
Author(s):  
Daniele Marinucci ◽  
Agnese Sbrollini ◽  
Ilaria Marcantoni ◽  
Micaela Morettini ◽  
Cees A. Swenne ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Artificial Neural Network ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Portable Devices ◽  
Promising Tool ◽  
Artificial Neural ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

scholarly journals Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3814
Author(s):  
Fangfang Jiang ◽  
Yihan Zhou ◽  
Tianyi Ling ◽  
Yanbing Zhang ◽  
Ziyu Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Detection Methods ◽  
Ecg Signals ◽  
Non Invasive ◽  
Cardiac Dynamics ◽  
Feature Extraction And Selection ◽  
Future Work ◽  
Electrocardiogram Ecg ◽  
Term Monitoring

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

scholarly journals Myocardial fibrosis and atrial fibrillation

2018 ◽  
pp. 71-76 ◽  
Author(s):  
S. V. Grigoryan ◽  
L. G. Azarapetyan ◽  
K. G. Adamyan
Keyword(s):  
Atrial Fibrillation ◽  
Growth Factor ◽  
Structural Changes ◽  
Transforming Growth Factor ◽  
Matrix Metalloproteases ◽  
Diagnostic Methods ◽  
Atrial Fibrosis ◽  
Non Invasive ◽  
Level Measurement

Atrial fibrillation is the most prevalent arrhythmia, and tends to progress. Any structural changes in the heart may lead to its progressive remodelling with increased deposition of connective tissue and fibrosis. Predominance of collagen types I and III synthesis over its degradation leads to accumulation of fibers and to fibrosis. Increase of atrial fibrosis is usually found on autopsy and biopsy. There is relation revealed, of atrial fibrosis grade and postsurgery atrial fibrillation. The mechanisms participating in the structural remodelling and progression of atrial fibrosis are not studied well enough, but there is known role of renin-angiotensinaldosterone system, transforming growth factor, inflammation and matrix metalloproteases. As an alternative, one should consider non-invasive diagnostic methods: magnetic resonance imaging of the heart and biomarkers level measurement. Hyperactivation of the renin-angiotensin-aldosterone system facilitates structural remodelling of the heart and progression of atrial fibrosis. Hyperexpression of the transforming growth factor leads to selective atrial fibrosis, heterogeneity of excitation conduction and fibrillation onset. Matrix metalloproteases are the marker of extracellular degradation. Study of fibrosis biomarkers makes it to increase significantly the efficacy of atrial fibrillation course prediction.

scholarly journals Non-invasive Estimation of Atrial Fibrillation Driver Position With Convolutional Neural Networks and Body Surface Potentials

2021 ◽  
Vol 12 ◽  
Author(s):  
Miguel Ángel Cámara-Vázquez ◽  
Ismael Hernández-Romero ◽  
Eduardo Morgado-Reyes ◽  
Maria S. Guillem ◽  
Andreu M. Climent ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Body Surface ◽  
Classification Problem ◽  
Surface Potentials ◽  
Non Invasive ◽  
Promising Tool ◽  
Body Surface Potential ◽  
Ill Posed

Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns, and AF onset locations and drivers responsible for its perpetuation are the main targets for ablation procedures. ECG imaging (ECGI) has been demonstrated as a promising tool to identify AF drivers and guide ablation procedures, being able to reconstruct the electrophysiological activity on the heart surface by using a non-invasive recording of body surface potentials (BSP). However, the inverse problem of ECGI is ill-posed, and it requires accurate mathematical modeling of both atria and torso, mainly from CT or MR images. Several deep learning-based methods have been proposed to detect AF, but most of the AF-based studies do not include the estimation of ablation targets. In this study, we propose to model the location of AF drivers from BSP as a supervised classification problem using convolutional neural networks (CNN). Accuracy in the test set ranged between 0.75 (SNR = 5 dB) and 0.93 (SNR = 20 dB upward) when assuming time independence, but it worsened to 0.52 or lower when dividing AF models into blocks. Therefore, CNN could be a robust method that could help to non-invasively identify target regions for ablation in AF by using body surface potential mapping, avoiding the use of ECGI.

Atrial flutter: management

ESC CardioMed ◽  
2018 ◽  
pp. 2211-2217
Author(s):  
Tilman Maurer ◽  
Christine Lemes ◽  
Karl-Heinz Kuck
Keyword(s):  
Atrial Flutter ◽  
Structural Heart Disease ◽  
Term Treatment ◽  
Non Invasive ◽  
Surface Ecg ◽  
Long Term Treatment ◽  
The Common ◽  
The Right ◽  
Electrocardiogram Ecg

Atrial flutter (AFL) is the most common macroreentry tachycardia in patients with and without structural heart disease. In the majority of cases, the arrhythmia is associated with a pre-existing comorbidity such as heart failure or lung disease. AFL refers to an electrocardiogram (ECG) pattern of a regular tachycardia with an atrial rate of more than 240 beats per minute and a lack of an isoelectric baseline between deflections. The most frequent form is termed ‘common’ or ‘typical’ if biphasic waves are present in the inferior leads, resembling a ‘saw-tooth’ pattern. Common AFL is diagnosed in 90% of cases and its mechanism is a macroreentry within the right atrium involving the cavotricuspid isthmus. ‘Atypical’ AFL refers to any ECG flutter morphology different from the common type. While the surface ECG provides a widely available and non-invasive diagnostic tool, a definite diagnosis of the underlying tachycardia mechanism can only be established by invasive electrophysiological testing. Acute management of AFL includes measures for rate control by pharmacological treatment or rhythm control by antiarrhythmic drugs or electrical cardioversion. For long-term treatment, catheter ablation offers a safe, effective, and curative approach for common flutter and is also a treatment option for atypical AFL. Anticoagulation should be initiated according to risk stratification based on the CHA2DS2-VASc score to prevent thromboembolic complications. This chapter provides a detailed overview on the pathophysiology and electrocardiographic characteristics of AFL and discusses the clinical management of the arrhythmia.

scholarly journals The experience of using non-invasive long-term ECG monitoring in patients with various cardiac arrhythmias

2021 ◽  
Vol 36 (3) ◽  
pp. 166-172
Author(s):  
E. A. Archakov ◽  
R. E. Batalov ◽  
S. Y. Usenkov ◽  
S. V. Popov
Keyword(s):  
Atrial Fibrillation ◽  
Cardiac Arrhythmias ◽  
Ecg Monitoring ◽  
Non Invasive ◽  
Electrocardiogram Ecg ◽  
Clinical Cases

The article describes clinical cases demonstrating the advantages of non-invasive long-term electrocardiogram (ECG) monitoring allowing to detect asymptomatic atrial fibrillation (AF) and transient atrioventricular (AV) and sinoatrial (SA) blocks.

scholarly journals Spatial characteristics of atrial fibrillation using the surface ECG

2007 ◽  
Author(s):  
U. Richter ◽  
M. Stridh ◽  
A. Bollmann ◽  
D. Husser ◽  
L. Sornmo
Keyword(s):  
Atrial Fibrillation ◽  
Spatial Characteristics ◽  
Surface Ecg
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