scholarly journals Single-Feature Method for Fast Atrial Fibrillation Detection in ECG Signals

2020 ◽  
Author(s):  
Lucie Marsanova ◽  
Lukas Smital ◽  
Radovan Smisek ◽  
Andrea Nemcova ◽  
Martin Vitek
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signals ◽  
Single Feature ◽  
Atrial Fibrillation Detection

scholarly journals Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 436
Author(s):  
Yunfei Cheng ◽  
Ying Hu ◽  
Mengshu Hou ◽  
Tongjie Pan ◽  
Wenwen He ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Compressed Sensing ◽  
Health Monitoring ◽  
Prior Information ◽  
Classification Performance ◽  
Experimental Results ◽  
Measurement Matrix ◽  
Ecg Signals ◽  
Proposed Model ◽  
Atrial Fibrillation Detection

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

scholarly journals SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongpo Zhang ◽  
Renke He ◽  
Honghua Dai ◽  
Mingliang Xu ◽  
Zongmin Wang
Keyword(s):  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Morbidity And Mortality ◽  
High Morbidity ◽  
Ecg Signal ◽  
Time Frequency ◽  
Ecg Signals ◽  
Convolution Process ◽  
The Time Domain ◽  
Atrial Fibrillation Detection

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified. In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments. We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy. The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform. The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

Atrial fibrillation detection by DFA and SDCST methods

2021 ◽  
Vol 16 (3) ◽  
pp. 189-196
Author(s):  
Regis Nunes Vargas ◽  
Antônio Cláudio Paschoarelli Veiga ◽  
Raquel Romes Linhares
Keyword(s):  
Atrial Fibrillation ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Training Set ◽  
Test Set ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Electrocardiogram Signal ◽  
Detrended Fluctuation ◽  
Atrial Fibrillation Detection

Many cardiac disorders were diagnosed by analyzing an electrocardiogram signal, in particular, atrial fibrillation. We join the SDCST method with the Detrended Fluctuation Analysis (DFA) and the backpropagation net to identify atrial fibrillation in one hundred ECG signals obtained from Physionet Challenge 2017 database. The accuracy of the proposed classifier parameter is 97% for the training set and 95% for the test set.

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

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