scholarly journals Automatic Arrhythmia Detection Using One-Dimensional Convolutional Neural Network

2020 ◽  
Author(s):  
Zhong Liu ◽  
Xin’an Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cardiac Activity ◽  
The Elderly ◽  
Accurate Method ◽  
Detection Algorithm ◽  
Dynamic Monitoring ◽  
Ecg Signal ◽  
One Dimensional ◽  
Ecg Signals

Abstract Background: Cardiovascular diseases (CVDs) are common diseases that pose significant threats to human health. Statistics have demonstrated that a large number of individuals die unexpectedly from sudden CVDs. Therefore, real-time monitoring and diagnosis of abnormal changes in cardiac activity are critical, as they can help the elderly and patients handle emergencies in a timely manner. To this end, a round-the-clock electrocardiogram (ECG) monitoring system can be developed with the quick detection of an ECG signal, segmentation of the detected ECG signal, and rapid diagnosis of a single segmented ECG beat. In this paper, to achieve the automatic detection and diagnosis of an ECG signal, five common types of ECG signals are used for recognition. For pre-processing the original ECG signal, the dual-slope detection algorithm is proposed and developed. Then, with the pre-processed ECG data, a five-layer one-dimensional convolutional neural network is constructed to classify five categories of heartbeats, namely, a normal heartbeat and four types of abnormal heartbeats. Results: To be able to compare the results of the experiment, the experimental data used in this study are obtained from the open-source MIT-BIH arrhythmia database. This database is authoritative, as each ECG signal cycle is annotated by at least two cardiologists, and abnormal ECG signals are classified into different categories. By comparing the detection and recognition results in this study with the results annotated in the MIT-BIH arrhythmia database, an overall accuracy of 96.20% is achieved in the classification of normal ECG signals and four categories of abnormal ECG signals.Conclusions: This paper provides an accurate method with low computational complexity for 24-hour dynamic monitoring and automated diagnosis of heartbeat conditions. With wearable devices, this method can be used at home for the initial screening of CVDs. In addition, it can perform diagnosis and warning for postoperative patients or patients with chronic CVDs.

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

2020 ◽  
Vol 10 (3) ◽  
pp. 976
Author(s):  
Rana N. Costandy ◽  
Safa M. Gasser ◽  
Mohamed S. El-Mahallawy ◽  
Mohamed W. Fakhr ◽  
Samir Y. Marzouk
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
P Wave ◽  
Ecg Signal ◽  
P Waves ◽  
Ecg Signals ◽  
Wave Detection ◽  
Automated Algorithm ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal analysis is a critical task in diagnosing the presence of any cardiac disorder. There are limited studies on detecting P-waves in various atrial arrhythmias, such as atrial fibrillation (AFIB), atrial flutter, junctional rhythm, and other arrhythmias due to P-wave variability and absence in various cases. Thus, there is a growing need to develop an efficient automated algorithm that annotates a 2D printed version of P-waves in the well-known ECG signal databases for validation purposes. To our knowledge, no one has annotated P-waves in the MIT-BIH atrial fibrillation database. Therefore, it is a challenge to manually annotate P-waves in the MIT-BIH AF database and to develop an automated algorithm to detect the absence and presence of different shapes of P-waves. In this paper, we present the manual annotation of P-waves in the well-known MIT-BIH AF database with the aid of a cardiologist. In addition, we provide an automatic P-wave segmentation for the same database using a fully convolutional neural network model (U-Net). This algorithm works on 2D imagery of printed ECG signals, as this type of imagery is the most commonly used in developing countries. The proposed automatic P-wave detection method obtained an accuracy and sensitivity of 98.56% and 98.78%, respectively, over the first 5 min of the second lead of the MIT-BIH AF database (a total of 8280 beats). Moreover, the proposed method is validated using the well-known automatically and manually annotated QT database (a total of 11,201 and 3194 automatically and manually annotated beats, respectively). This results in accuracies of 98.98 and 98.9%, and sensitivities of 98.97 and 97.24% for the automatically and manually annotated QT databases, respectively. Thus, these results indicate that the proposed automatic method can be used for analyzing long-printed ECG signals on mobile battery-driven devices using only images of the ECG signals, without the need for a cardiologist.

scholarly journals Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2558 ◽  
Author(s):  
Yinsheng Ji ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
System Development ◽  
Support Vector ◽  
One Dimensional ◽  
Ecg Signals ◽  
Two Dimensional Image ◽  
Average Accuracy ◽  
Effective System ◽  
The One

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutional neural network (Faster R-CNN) algorithm. The original one-dimensional ECG signals contain the preprocessed patient ECG signals and some ECG recordings from the MIT-BIH database in this experiment. Each ECG beat of one-dimensional ECG signals was transformed into a two-dimensional image for experimental training sets and test sets. As a result, we classified the ECG beats into five categories with an average accuracy of 99.21%. In addition, we did a comparative experiment using the one versus rest support vector machine (OVR SVM) algorithm, and the classification accuracy of the proposed Faster R-CNN was shown to be 2.59% higher.

scholarly journals A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning

2021 ◽  
Author(s):  
Abdulhamit Subasi ◽  
Sengul Dogan ◽  
Turker Tuncer
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Graph Transformation ◽  
Signal Classification ◽  
Ecg Signal ◽  
One Dimensional ◽  
Ecg Signals ◽  
Feature Selection Approach ◽  
Accuracy Rates ◽  
Adaptive Pattern

AbstractElectrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.

scholarly journals Development of deep CNN architecture for anomalies in ECG signals

2020 ◽  
Vol 11 (3) ◽  
pp. 3490-3495
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Anshika Singh ◽  
Ankit Dash ◽  
Ayushi Sharma
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Time Interval ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Poincaré Plot ◽  
Ecg Signals ◽  
Test Input ◽  
Deep Cnn

In this paper, we propose a deep learning convolutional neural network (CNN) approach to classify arrhythmia based on the time interval of the QRS complex of the ECG signal. The ECG signal was denoised using multiple filters based on the Pan Tompkins algorithm. QRS detection has been done using Pan Tompkins Algorithm. Then the QRS complex is identified using local peaks based technique inside the layers of the Convolutional Neural Network where the repeated application of the same filter to our input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input which in our case is the changes in the q-s time interval. Based on the R-R time interval, Heart rate variability (HRV) was computed, and Poincare plot was generated. Instead of using raw ECG signal to train the CNN, we used the feature extracted from ECG signal obtained from Physionet database to train the CNN and map the pattern changes for different classes of diseases. The classifier was then used to classify the test input as either or normal, tachyarrhythmia or intracardiac atrial fibrillation. Data acquisition, ECG data pre-processing and CNN classifier are the several methods that are involved for the classification of several arrhythmias.

scholarly journals Searching for Premature Ventricular Contraction from Electrocardiogram by Using One-Dimensional Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1790
Author(s):  
Junsheng Yu ◽  
Xiangqing Wang ◽  
Xiaodong Chen ◽  
Jinglin Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Time Window ◽  
Premature Ventricular Contraction ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Ventricular Contraction ◽  
Test Set ◽  
One Dimensional

Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing.

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