Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network

2021 ◽  
Author(s):  
Hawraa Al Qaraghuli ◽  
Reza Sheibani ◽  
Hamid Tabatabaee
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Variable Length

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 Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Genetic algorithm‐optimised structure of convolutional neural network for face recognition applications

2016 ◽  
Vol 10 (6) ◽  
pp. 559-566 ◽  
Author(s):  
Arash Rikhtegar ◽  
Mohammad Pooyan ◽  
Mohammad Taghi Manzuri‐Shalmani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Face Recognition ◽  
Convolutional Neural Network

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.

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