scholarly journals Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7230
Author(s):  
Catalin Dumitrescu ◽  
Ilona-Madalina Costea ◽  
Angel-Ciprian Cormos ◽  
Augustin Semenescu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Computation Time ◽  
Automatic Detection ◽  
Cortical Arousal ◽  
Eeg Signals ◽  
Parkinson’S Diseases ◽  
Time Required ◽  
Cohen Class ◽  
Reliable Methods

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Automatic detection of anatomical landmarks in brain MR scanning using multi-task deep neural networks

2020 ◽  
Vol 396 ◽  
pp. 514-521 ◽  
Author(s):  
Xulei Yang ◽  
Wai Teng Tang ◽  
Gabriel Tjio ◽  
Si Yong Yeo ◽  
Yi Su
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Detection ◽  
Anatomical Landmarks ◽  
Mr Scanning

Learning the forward kinematics behavior of a hybrid robot employing artificial neural networks

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 847-855 ◽  
Author(s):  
Rongjie Kang ◽  
Hélène Chanal ◽  
Thomas Bonnemains ◽  
Sylvain Pateloup ◽  
David T. Branson ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Kinematic Model ◽  
Computation Time ◽  
Forward Kinematics ◽  
The Neural Network ◽  
Automotive Assembly ◽  
Aircraft Component ◽  
Simulation And Experiment ◽  
Time Required

SUMMARYHybrid robots, composed of a parallel platform and serial wrist, achieve a compromise of stiffness and dexterity. Thus, they are well suited for applications such as aircraft component machining and automotive assembly, where high accuracy and large workspace movements are required. However, their forward kinematics can be highly coupled and be nonlinear. To reduce the time required to define the forward kinematics of a robot with parallel–serial structure, this paper introduces the use of neural networks. Two radial basis function networks are trained to learn the parallel and serial kinematics separately, and then integrated into a complete model. The error of this network model is analyzed and identified by a particle swarm optimization algorithm. Simulation and experiment results are obtained from the hybrid robot, Exechon, which shows that the developed kinematic model is able to produce accurate position and orientation estimates of the end-effector. The computation time of the neural network model is greatly reduced when compared to the time achieved by the numerical model.

scholarly journals Towards a Better Basis Search through a Surrogate Model-Based Epistasis Minimization for Pseudo-Boolean Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1287
Author(s):  
Yong-Hoon Kim ◽  
Yourim Yoon ◽  
Yong-Hyuk Kim
Keyword(s):  
Neural Networks ◽  
Surrogate Model ◽  
Deep Neural Networks ◽  
Computation Time ◽  
Model Based ◽  
All Solutions ◽  
Boolean Optimization

Epistasis, which indicates the difficulty of a problem, can be used to evaluate the basis of the space in which the problem lies. However, calculating epistasis may be challenging as it requires all solutions to be searched. In this study, a method for constructing a surrogate model, based on deep neural networks, that estimates epistasis is proposed for basis evaluation. The proposed method is applied to the Variant-OneMax problem and the NK-landscape problem. The method is able to make successful estimations on a similar level to basis evaluation based on actual epistasis, while significantly reducing the computation time. In addition, when compared to the epistasis-based basis evaluation, the proposed method is found to be more efficient.

scholarly journals Automatic detection of constructions using binary image segmentation algorithms

2019 ◽  
pp. 264-268
Author(s):  
E A Dmitriev ◽  
A A Borodinov ◽  
A I Maksimov ◽  
S A Rychazhkov
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Detection ◽  
Aerial Images ◽  
Optimal Model ◽  
Training Time ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
Binary Segmentation ◽  
And Training

This article presents binary segmentation algorithms for buildings automatic detection on aerial images. There were conducted experiments among deep neural networks to find the most effective model in sense of segmentation accuracy and training time. All experiments were conducted on Moscow region images that were got from open database. As the result the optimal model was found for buildings automatic detection.

DeepVarveNet: Automatic detection of glacial varves with deep neural networks

2020 ◽  
Vol 144 ◽  
pp. 104584
Author(s):  
Anna Fabijańska ◽  
Andrew Feder ◽  
John Ridge
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Detection
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