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

scholarly journals ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation (AF) with Deep Visual Attention

2019 ◽  
Author(s):  
Sajad Mousavi
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Ecg Signal ◽  
Learning Approaches ◽  
Current Signal ◽  
High Level ◽  
The Given

The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results. Deep neural networks have shown to be very powerful to learn the non-linear patterns in the data. While a deep learning approach attempts to learn complex pattern related to the presence of AF in the ECG, they can benefit from knowing which parts of the signal is more important to focus during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect AF presented in the ECG signal. The first channel takes in a preprocessed ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the preprocessed ECG signal to consider all features of entire signals. The model shows via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. In addition, this combination significantly improves the performance of the atrial fibrillation detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.)

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.

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.

scholarly journals Atrial Fibrillation Detection Using Feedforward Neural Network

2021 ◽  
Author(s):  
Yunfan Chen ◽  
Chong Zhang ◽  
Chengyu Liu ◽  
Yiming Wang ◽  
Xiangkui Wan
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Detection System ◽  
Recognition Rate ◽  
Feedforward Neural Network ◽  
Classification Model ◽  
Detection Sensitivity ◽  
Ecg Signal ◽  
Atrial Fibrillation Detection

Abstract Atrial fibrillation is one of the most common arrhythmias in clinics, which has a great impact on people's physical and mental health. Electrocardiogram (ECG) based arrhythmia detection is widely used in early atrial fibrillation detection. However, ECG needs to be manually checked in clinical practice, which is time-consuming and labor-consuming. It is necessary to develop an automatic atrial fibrillation detection system. Recent research has demonstrated that deep learning technology can help to improve the performance of the automatic classification model of ECG signals. To this end, this work proposes effective deep learning based technology to automatically detect atrial fibrillation. First, novel preprocessing algorithms of wavelet transform and sliding window filtering (SWF) are introduced to reduce the noise of the ECG signal and to filter high-frequency components in the ECG signal, respectively. Then, a robust R-wave detection algorithm is developed, which achieves 99.22% detection sensitivity, 98.55% positive recognition rate, and 2.25% deviance on the MIT-BIH arrhythmia database. In addition, we propose a feedforward neural network (FNN) to detect atrial fibrillation based on ECG records. Experiments verified by a 10-fold cross-validation strategy show that the proposed model achieves competitive detection performance and can be applied to wearable detection devices. The proposed atrial fibrillation detection model achieves an accuracy of 84.00%, the detection sensitivity of 84.26%, the specificity of 93.23%, and the area under the receiver working curve of 89.40% on the mixed dataset composed of Challenge2017 database and MIT-BIH arrhythmia database.

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