Using a One-dimensional Convolution Neural Network to Detect Atrial Fibrillation

2021 ◽  
Author(s):  
Aslan A. Tatarkanov ◽  
Islam A. Alexandrov ◽  
Afina A. Bestavashvili ◽  
Philip Yu. Kopylov
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Convolution Neural Network ◽  
One Dimensional

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.

scholarly journals New artificial intelligence prediction model using serial prothrombin time international normalized ratio measurements in atrial fibrillation patients on vitamin K antagonists: GARFIELD-AF

2019 ◽  
Vol 6 (5) ◽  
pp. 301-309 ◽  
Author(s):  
Shinichi Goto ◽  
Shinya Goto ◽  
Karen S Pieper ◽  
Jean-Pierre Bassand ◽  
Alan John Camm ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Prothrombin Time ◽  
Clinical Outcomes ◽  
Vitamin K ◽  
Vitamin K Antagonists ◽  
International Normalized Ratio ◽  
One Dimensional ◽  
Major Bleed

Abstract Aims Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes.

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

scholarly journals Quantification of Mental Workload Using a Cascaded Deep One-dimensional Convolution Neural Network and Bi-directional Long Short-Term Memory Model

2021 ◽  
Author(s):  
Vipul Sharma ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Mental Workload ◽  
Binary Classification ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

In this paper, a new cascade one-dimensional convolution neural network (1DCNN) and bidirectional long short-term memory (BLSTM) model has been developed for binary and ternary classification of mental workload (MWL). MWL assessment is important to increase the safety and efficiency in Brain-Computer Interface (BCI) systems and professions where multi-tasking is required. Keeping in mind the necessity of MWL assessment, a two-fold study is presented, firstly binary classification is done to classify MWL into Low and High classes. Secondly, ternary classification is applied to classify MWL into Low, Moderate, and High classes. The cascaded 1DCNN-BLSTM deep learning architecture has been developed and tested over the Simultaneous task EEG workload (STEW) dataset. Unlike recent research in MWL, handcrafted feature extraction and engineering are not done, rather end-to-end deep learning is used over 14 channel EEG signals for classification. Accuracies exceeding the previous state-of-the-art studies have been obtained. In binary and ternary classification accuracies of 96.77% and 95.36% have been achieved with 7-fold cross validation, respectively.

scholarly journals ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification

2021 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Byungjin Kang ◽  
Inho Park ◽  
Changmin Ok ◽  
Sungho Kim
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Convolution Neural Network ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
One Dimensional ◽  
Proposed Model ◽  
3D Cnn ◽  
2D And 3D

Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information.

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