scholarly journals System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

2021 ◽  
Vol 11 (16) ◽  
pp. 7213
Author(s):  
Pavel Lyakhov ◽  
Mariya Kiladze ◽  
Ulyana Lyakhova
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Network Architecture ◽  
Short Term Memory ◽  
Developed Countries ◽  
Neural Network Architecture ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Entropy Functions

Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

scholarly journals Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1086
Author(s):  
Raoul Hoffmann ◽  
Hanna Brodowski ◽  
Axel Steinhage ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Capacitive Sensor ◽  
Legged Locomotion ◽  
Sensor Data ◽  
Neural Network Architecture ◽  
Gait Patterns ◽  
Before And After

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person’s state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.

scholarly journals Artificial Recurrent Neural Network Architecture in Customer Consumption Prediction for Business Development

2020 ◽  
Vol 2 (2) ◽  
pp. 111-120
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Association Rule ◽  
Network Architecture ◽  
Short Term Memory ◽  
Real Life ◽  
Consumption Pattern ◽  
Neural Network Architecture ◽  
Competitive Edge ◽  
Frame Work

The customer consumption pattern prediction has become one of a significant role in developing the business and taking it to a competitive edge. For forecasting the behaviors of the consumers the paper engages an artificial recurrent neural network architecture the long short-term memory an improvement of recurrent neural network. The mechanism laid out to predict the pattern of the consumption, uses the information’s about the consumption of products based on the age and the gender. The information essential are extracted and described with the prefix-span procedure based association rule. Utilizing the information about the day to day products purchase pattern as input a frame work to predict the customer daily essentials was designed, the designed frame was capable enough to learn the dissimilarities across the predicted and the original miscalculation rates. The frame work devised was tested using real life applications and the results observed demonstrated that the proposed LSTM based prediction with the prefix span association rule to acquire the day today consumption details is compatible for forecasting the customer consumption over time accurately.

Multi-feature Fusion of Deep Neural Network for Screening Atrial Fibrillation Using ECG Signals

2021 ◽  
Author(s):  
Xingxiang Tao ◽  
Hao Dang ◽  
Xiangdong Xu ◽  
Xiaoguang Zhou ◽  
Danqun Xiong
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Diagnostic Tools ◽  
Data Set ◽  
Ecg Signals ◽  
Proposed Model ◽  
Unseen Data

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it can cause a variety of cardiovascular diseases. This brings great hidden danger to people’s health and life safety all over the world. Electrocardiogram (ECG) is one of the most important noninvasive diagnostic tools for heart disease. Accurate interpretation of ECG is particularly important for the detection and treatment of AF. It is valuable to develop an efficient, accurate, and stable automatic AF detection algorithm in clinical settings. Therefore, this article proposes a novel integrated module, which combines densely connected convolutional network (DenseNet) module and bidirectional long short-term memory (BLSTM) module, based on the excellent ability of BLSTM on extracting the time series features, while DenseNet on capturing local features. Furthermore, we also propose a novel network architecture (MF-DenseNet–BLSTM) based on the integrated module mentioned above and multi-feature fusion for automatic AF detection using the ECG signals. The proposed model employs the architecture of dual-stream deep neural network to fusing multiple features. Specifically, the network of each stream structure consists of two parts with DenseNet module and BLSTM module. The data sets used to validate and test the proposed model are from the MIT-BIH Atrial Fibrillation Database. The experimental results show that the proposed model achieved 98.81% accuracy in training set, and achieved 98.04% accuracy in the testing set which is unseen data set. The proposed MF-DenseNet–BLSTM has shown excellent robustness and accuracy in automatic AF detection.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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