Atrial Fibrillation and Premature Contraction Classification Using Convolutional Neural Network

2019 ◽  
Author(s):  
Sukhdev Singh ◽  
R.K Sunkaria ◽  
B.S. Saini ◽  
Kapil Kumar
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network

scholarly journals AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bambang Tutuko ◽  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Muhammad Naufal Rachmatullah ◽  
Annisa Darmawahyuni ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
Learning System ◽  
Single Frequency ◽  
Feature Maps ◽  
Normal Sinus ◽  
Unseen Data ◽  
Specific Device ◽  
Model Formation

Abstract Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment

scholarly journals AFibNet: An Implementation of Atrial Fibrillation Detection With Convolutional Neural Network

2021 ◽  
Author(s):  
Bambang Tutuko ◽  
Siti Nurmaini ◽  
Alexander Edo Tondas ◽  
Muhammad Naufal Rachmatullah ◽  
Annisa Darmawahyuni ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolutional Neural Network ◽  
Learning System ◽  
Single Frequency ◽  
Feature Maps ◽  
Proposed Model ◽  
Unseen Data ◽  
Specific Device ◽  
Model Formation

Abstract Background: Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R-R intervals to determine the Heart Rate Variability (HRV). An accurate HRV is the gold standard for predicting the AF condition. Hence, we propose a DL approach to analyze massive amounts of ECG raw data in a broad range of devices to overcome a current challenge.Results: This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8,232 records based on three datasets and tested with 3,610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. When tested with unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity in 0.02 seconds for one instance when processed in theDL-Cloud System.Conclusions: These findings demonstrate that the proposed model approach can used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment.

scholarly journals Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Ricardo Salinas-Martínez ◽  
Johannes de Bie ◽  
Nicoletta Marzocchi ◽  
Frida Sandberg
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Thrombus Formation ◽  
Normal Sinus Rhythm ◽  
Morphological Characteristics ◽  
P Wave ◽  
Learning Approaches ◽  
Normal Sinus

Background: Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

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 Classification of Atrial Fibrillation and Congestive Heart Failure using Convolutional Neural Network with Electrocardiogram

2021 ◽  
Author(s):  
Yunendah Nur Fu’adah ◽  
Ki Moo Lim
Keyword(s):  
Neural Network ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Convolutional Neural Network ◽  
Classification System ◽  
Normal Sinus Rhythm ◽  
Delayed Diagnosis ◽  
Ecg Signals

Abstract Delayed diagnosis of atrial fibrillation (AF) and congestive heart failure (CHF) can lead to death. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. Several studies have reported promising results using the automatic classification of ECG signals. The performance accuracy needs to be improved considering that an accurate classification system of AF and CHF has the potential to save a patient’s life. An optimal ECG signal classification system for AF and CHF has been proposed in this study using a one-dimensional convolutional neural network (1-D CNN) to improve the performance. A total of 150 datasets of ECG signals were modeled using the1-D CNN. The proposed 1-D CNN algorithm, provided precision values, recall, f1-score, accuracy of 100%, and successfully classified raw data of ECG signals into three conditions, which are normal sinus rhythm (NSR), AF, and CHF. The results showed that the proposed method outperformed the previous methods. This approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

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