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 ECG Heartbeat Classification Based on an Improved ResNet-18 Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Enbiao Jing ◽  
Haiyang Zhang ◽  
ZhiGang Li ◽  
Yazhi Liu ◽  
Zhanlin Ji ◽  
...  
Keyword(s):  
State Of The Art ◽  
Classification Performance ◽  
Classification Models ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
Residual Structure ◽  
Proposed Model ◽  
Model Training ◽  
Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

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.

scholarly journals Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

2019 ◽  
Vol 8 (11) ◽  
pp. 1840 ◽  
Author(s):  
Jesús Pérez-Valero ◽  
M. Victoria Caballero Pintado ◽  
Francisco Melgarejo ◽  
Antonio-Javier García-Sánchez ◽  
Joan Garcia-Haro ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ambulatory Monitoring ◽  
Ecg Signals ◽  
Predictive Algorithm ◽  
Predictive Algorithms ◽  
Proposed Model ◽  
Quantification Analysis ◽  
Fitting In ◽  
Electrocardiogram Ecg ◽  
Fold Cross Validation

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

scholarly journals An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Chen ◽  
Wei Liang ◽  
Wenjia Yang
Keyword(s):  
Atrial Fibrillation ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Sequence Data ◽  
Detection System ◽  
Heart Rhythm ◽  
Classification Performance ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

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

Multi-feature Fusion of Deep Neural Network for Screening Atrial Fibrillation Using ECG Signals

2021 ◽  
Author(s):  
Xingxiang Tao ◽  
Hao Dang ◽  
Xiangdong Xu ◽  
Xiaoguang Zhou ◽  
Danqun Xiong
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Diagnostic Tools ◽  
Data Set ◽  
Ecg Signals ◽  
Proposed Model ◽  
Unseen Data

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it can cause a variety of cardiovascular diseases. This brings great hidden danger to people’s health and life safety all over the world. Electrocardiogram (ECG) is one of the most important noninvasive diagnostic tools for heart disease. Accurate interpretation of ECG is particularly important for the detection and treatment of AF. It is valuable to develop an efficient, accurate, and stable automatic AF detection algorithm in clinical settings. Therefore, this article proposes a novel integrated module, which combines densely connected convolutional network (DenseNet) module and bidirectional long short-term memory (BLSTM) module, based on the excellent ability of BLSTM on extracting the time series features, while DenseNet on capturing local features. Furthermore, we also propose a novel network architecture (MF-DenseNet–BLSTM) based on the integrated module mentioned above and multi-feature fusion for automatic AF detection using the ECG signals. The proposed model employs the architecture of dual-stream deep neural network to fusing multiple features. Specifically, the network of each stream structure consists of two parts with DenseNet module and BLSTM module. The data sets used to validate and test the proposed model are from the MIT-BIH Atrial Fibrillation Database. The experimental results show that the proposed model achieved 98.81% accuracy in training set, and achieved 98.04% accuracy in the testing set which is unseen data set. The proposed MF-DenseNet–BLSTM has shown excellent robustness and accuracy in automatic AF detection.

scholarly journals An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 121 ◽  
Author(s):  
Zhenyu Zheng ◽  
Zhencheng Chen ◽  
Fangrong Hu ◽  
Jianming Zhu ◽  
Qunfeng Tang ◽  
...  
Keyword(s):  
Input Data ◽  
Short Term Memory ◽  
Normal Sinus Rhythm ◽  
Classification Performance ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Normal Sinus ◽  
Specificity And Sensitivity ◽  
Proposed Model ◽  
Signal Evaluation

Electrocardiogram (ECG) signal evaluation is routinely used in clinics as a significant diagnostic method for detecting arrhythmia. However, it is very labor intensive to externally evaluate ECG signals, due to their small amplitude. Using automated detection and classification methods in the clinic can assist doctors in making accurate and expeditious diagnoses of diseases. In this study, we developed a classification method for arrhythmia based on the combination of a convolutional neural network and long short-term memory, which was then used to diagnose eight ECG signals, including a normal sinus rhythm. The ECG data of the experiment were derived from the MIT-BIH arrhythmia database. The experimental method mainly consisted of two parts. The input data of the model were two-dimensional grayscale images converted from one-dimensional signals, and detection and classification of the input data was carried out using the combined model. The advantage of this method is that it does not require performing feature extraction or noise filtering on the ECG signal. The experimental results showed that the implemented method demonstrated high classification performance in terms of accuracy, specificity, and sensitivity equal to 99.01%, 99.57%, and 97.67%, respectively. Our proposed model can assist doctors in accurately detecting arrhythmia during routine ECG screening.

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