APPLYING A DEEP NEURAL NETWORK FOR AUTOMATED PREDICTION OF PAROXYSMAL ATRIAL FIBRILLATION ONSET

2020 ◽  
Vol 36 (10) ◽  
pp. S42-S43
Author(s):  
S. Safabakhsh ◽  
R. Zhao ◽  
Z. Laksman
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
Deep Neural Network ◽  
Automated Prediction

Reliable detection of atrial fibrillation with a medical wearable under inpatient conditions (CoMMoD-A-fib)

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Jacobsen ◽  
T.A Dembek ◽  
A.P Ziakos ◽  
G Kobbe ◽  
M Kollmann ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Neural Network ◽  
Sampling Rate ◽  
Public Institution ◽  
Funding Source ◽  
Upper Arm ◽  
Recording Time ◽  
Reduced Ejection Fraction ◽  
Non Invasive

Abstract Background Atrial fibrillation (A-fib) is the most common arrhythmia; however, detection of A-fib is a challenge due to irregular occurrence. Purpose Evaluating feasibility and performance of a non-invasive medical wearable for detection of A-fib. Methods In the CoMMoD-A-fib trial admitted patients with a high risk for A-fib carried the wearable and an ECG Holter (control) in parallel over a period of 24 hours under not physically restricted conditions. The wearable with a tight-fit upper arm band employs a photoplethysmography (PPG) technology enabling a high sampling rate. Different algorithms (including a deep neural network) were applied to 5 min PPG datasets for detection of A-fib. Proportion of monitoring time automatically interpretable by algorithms (= interpretable time) was analyzed for influencing factors. Results In 102 inpatients (age 71.0±11.9 years; 52% male) 2306 hours of parallel recording time could be obtained; 1781 hours (77.2%) of these were automatically interpretable by an algorithm analyzing PPG derived intervals. Detection of A-Fib was possible with a sensitivity of 92.7% and specificity of 92.4% (AUC 0.96). Also during physical activity, detection of A-fib was sufficiently possible (sensitivity 90.1% and specificity 91.2%). Usage of the deep neural network improved detection of A-fib further (sensitivity 95.4% and specificity 96.2%). A higher prevalence of heart failure with reduced ejection fraction was observed in patients with a low interpretable time (p=0.080). Conclusion Detection of A-fib by means of an upper arm non-invasive medical wearable with a high resolution is reliably possible under inpatient conditions. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Internal grant program (PhD and Dr. rer. nat. Program Biomedicine) of the Faculty of Health at Witten/Herdecke University, Germany. HELIOS Kliniken GmbH (Grant-ID 047476), Germany

scholarly journals Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoling Wei ◽  
Jimin Li ◽  
Chenghao Zhang ◽  
Ming Liu ◽  
Peng Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Complex Network ◽  
Individual Variation ◽  
Deep Neural Network ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Electrocardiogram Signal ◽  
Sensitivity Specificity ◽  
Atrial Fibrillation Detection

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

scholarly journals Methodology for detection of paroxysmal atrial fibrillation based on P-Wave, HRV and QR electrical alternans features

2020 ◽  
Vol 10 (4) ◽  
pp. 4023
Author(s):  
Henry Castro ◽  
Juan David Garcia-Racines ◽  
Alvaro Bernal-Noreña
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Paroxysmal Atrial Fibrillation ◽  
P Wave ◽  
Computational Techniques ◽  
K Nearest Neighbors ◽  
Ecg Signals ◽  
Complex Process ◽  
Artificial Neural Network Ann ◽  
Electrical Alternans

The detection of Paroxysmal Atrial Fibrillation (PAF) is a fairly complex process performed manually by cardiologists or electrophysiologists by reading an electrocardiogram (ECG). Currently, computational techniques for automatic detection based on fast Fourier transform (FFT), Bayes optimal classifier (BOC), k-nearest neighbors (K-NNs), and artificial neural network (ANN) have been proposed. In this study, six features were obtained based on the morphology of the P-Wave, the QRS complex and the heart rate variability (HRV) of the ECG. The performance of this methodology was validated using clinical ECG signals from the Physionet arrhythmia database MIT-BIH. A feedforward neural network was used to detect the presence of PAF reaching a general accuracy of 97.4%. The results obtained show that the inclusion of the information of the P-Wave, HRV and QR Electrical alternans increases the accuracy to identify the PAF event compared to other works that use the information of only one or at most two of them.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

2020 ◽  
Vol 44 (6) ◽  
Author(s):  
S. K. Ghosh ◽  
R. K. Tripathy ◽  
Mario R. A. Paternina ◽  
Juan J. Arrieta ◽  
Alejandro Zamora-Mendez ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Filter Bank ◽  
Deep Neural Network ◽  
Ecg Signal

Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network

2020 ◽  
Vol 116 ◽  
pp. 103378 ◽  
Author(s):  
Wenjuan Cai ◽  
Yundai Chen ◽  
Jun Guo ◽  
Baoshi Han ◽  
Yajun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Neural Network ◽  
Accurate Detection

scholarly journals ATRIAL FIBRILLATION DETECTION ON ELECTROCARDIOGRAMS WITH CONVOLUTIONAL NEURAL NETWORKS

2019 ◽  
Vol 9 (4) ◽  
pp. 69-73
Author(s):  
Viktor Kifer ◽  
Natalia Zagorodna ◽  
Olena Hevko
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Deep Neural Network ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Noisy Signals ◽  
Network Usage ◽  
Model Training

In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.

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