scholarly journals Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
N. Sriraam
Keyword(s):  
Neural Network ◽  
Single Layer ◽  
Clinical Information ◽  
Eeg Signals ◽  
Neural Network Models ◽  
Compression Scheme ◽  
Long Distance ◽  
Telemedicine System ◽  
On Demand ◽  
Reconstructed Signal

A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

Double Multi-Head Attention-Based Capsule Network for Relation Classification

2021 ◽  
Author(s):  
Hongjun Heng ◽  
Renjie Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Language Processing ◽  
Layer Structure ◽  
Single Layer ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Comparable Performance ◽  
Relation Classification

Semantic relation classification is an important task in the field of nature language processing. The existing neural network relation classification models introduce attention mechanism to increase the importance of significant features, but part of these attention models only have one head which is not enough to capture more distinctive fine-grained features. Models based on RNN (Recurrent Neural Network) usually use single-layer structure and have limited feature extraction capability. Current RNN-based capsule networks have problem of improper handling of noise which increase complexity of network. Therefore, we propose a capsule network relation classification model based on double multi-head attention. In this model, we introduce an auxiliary BiGRU (Bidirectional Gated Recurrent Unit) to make up for the lack of feature extraction performance of single BiGRU, improve the bilinear attention through double multihead mechanism to enable the model to obtain more information of sentence from different representation subspace and instantiate capsules with sentence-level features to alleviate noise impact. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model outperforms most of previous state-of-the-art neural network models and achieves the comparable performance with F1 score of 85.3% in capsule network.

EEG SIGNAL COMPRESSION USING RADIAL BASIS NEURAL NETWORKS

2004 ◽  
Vol 04 (02) ◽  
pp. 143-152 ◽  
Author(s):  
N. SRIRAAM ◽  
C. ESWARAN
Keyword(s):  
Neural Network ◽  
Radial Basis Function Network ◽  
Multilayer Perceptrons ◽  
Generalized Regression Neural Network ◽  
Neural Network Models ◽  
Compression Scheme ◽  
Two Stage ◽  
Linear Predictor ◽  
Radial Basis ◽  
Generalized Regression

This paper describes a two-stage lossless compression scheme for electroencephalographic (EEG) signals using radial basis neural network predictors. Two variants of the radial basis network, namely, the radial basis function network and the generalized regression neural network are used in the first stage and their performances are evaluated in terms of compression ratio. The training is imparted to the network by using two training schemes, namely, single block scheme and block adaptive scheme. The compression ratios achieved by these networks when used along with arithmetic encoders in a two-stage compression scheme are obtained for different EEG test files. It is found that the generalized regression neural network performs better than other neural network models such as multilayer perceptrons and Elman network and linear predictor such as FIR.

Learning Chinese Word Segmentation Based on Bidirectional GRU-CRF and CNN Network Model

2019 ◽  
Vol 15 (3) ◽  
pp. 47-62 ◽  
Author(s):  
Chenghai Yu ◽  
Shupei Wang ◽  
Jiajun Guo
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Neural Network Models ◽  
Long Distance ◽  
Training Time

Chinese word segmentation is the basis of the Chinese natural language processing (NLP). With the development of the deep learning, various neural network models are applied to the Chinese word segmentation. However, current neural network models have the characteristics of artificial feature extraction, nonstandard word-weight, inability to effectively use long-distance information and long training time of models in Chinese word segmentation. To solve a series of problems, this article presents a CNN-Bidirectional GRU-CRF neural network model (CNN Bidirectional GRU CRF Network, CBiGCN), which breaks through the limit of conventional method window, truly realizes end-to-end processing and applies to the neural network model by the five-Tag set method, bias-variable-weight greedy strategy and supplements by Goldstein-Armijo guidelines. Besides, this model, with simple structure, is easy to be operated. And it can automatically learn features, reduces large amounts of tasks on specific knowledge in the form of handcrafted features and data pre-processing, makes use of context information effectively. The authors set an experiment with two data corpuses for Chinese word segmentation to evaluate their system. The experiment verified their new model can obtain better Chinese word segmentation results and greatly reduce training time.

scholarly journals Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

2021 ◽  
Author(s):  
Neel Patel ◽  
William Bush
Keyword(s):  
Neural Network ◽  
Transcription Factor ◽  
Prediction Models ◽  
Network Models ◽  
Expression Regulation ◽  
Neural Network Models ◽  
Long Distance ◽  
Lymphoblastoid Cells ◽  
Chromatin Looping ◽  
Detection Approach

Background: Transcription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells. Results: We estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. Conclusion: Here, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

Gated Hierarchical LSTMs for Target-Based Sentiment Analysis

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1719-1737
Author(s):  
Hao Wang ◽  
Xiaofang Zhang ◽  
Bin Liang ◽  
Qian Zhou ◽  
Baowen Xu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Model ◽  
Attention Mechanism ◽  
Support Vector ◽  
Neural Network Models ◽  
Long Distance ◽  
Sentence Level

In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.

scholarly journals A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
N. Sriraam
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
High Performance ◽  
Digital Health ◽  
Error Modeling ◽  
Eeg Signals ◽  
Compression Scheme ◽  
Diagnostic Reliability ◽  
Compression Efficiency ◽  
Eeg Data

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

scholarly journals Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

2020 ◽  
Vol 2 (1) ◽  
pp. 1-19
Author(s):  
Kaixu Yang ◽  
Tapabrata Maiti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Feature Vector ◽  
Single Layer ◽  
Training Data ◽  
Classification Model ◽  
High Dimensional ◽  
Neural Network Models ◽  
Artificial Neural

An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from magnetic resonance imaging, spatial data and computer vision tasks. Most commonly used ANNs assume the availability of large training data compared to the dimension of feature vector. However, in modern applications, as mentioned above, the training sample sizes are often low, and may be even lower than the dimension of feature vector. In this paper, we consider a single layer ANN classification model that is suitable for analyzing high-dimensional low sample-size (HDLSS) data. We investigate the theoretical properties of the sparse group lasso regularized neural network and show that under mild conditions, the classification risk converges to the optimal Bayes classifier’s risk (universal consistency). Moreover, we proposed a variation on the regularization term. A few examples in popular research fields are also provided to illustrate the theory and methods.

scholarly journals Detecting global influence of transcription factor interactions on gene expression in lymphoblastoid cells using neural network models

2021 ◽  
Author(s):  
Neel Patel ◽  
William S. Bush
Keyword(s):  
Neural Network ◽  
Transcription Factor ◽  
Prediction Models ◽  
Network Models ◽  
Expression Regulation ◽  
Neural Network Models ◽  
Long Distance ◽  
Lymphoblastoid Cells ◽  
Chromatin Looping ◽  
Detection Approach

Abstract Background Transcription factor(TF) interactions are known to regulate target gene(TG) expression in eukaryotes via TF regulatory modules(TRMs). Such interactions can be formed due to co-localizing TFs binding proximally to each other in the DNA sequence or over long distances between distally binding TFs via chromatin looping. While the former type of interaction has been characterized extensively, long distance TF interactions are still largely understudied. Furthermore, most prior approaches have focused on characterizing physical TF interactions without accounting for their effects on TG expression regulation. Understanding TRM based TG expression regulation could aid in understanding diseases caused by disruptions to these mechanisms. In this paper, we present a novel neural network based TRM detection approach that consists of using multi-omics TF based regulatory mechanism information to generate features for building non-linear multilayer perceptron TG expression prediction models in the GM12878 immortalized lymphoblastoid cells.Results We estimated main effects of 149 individual TFs and interaction effects of 48 distinct combinations of TFs forming TRMs based on their influence on TG expression. We identified several well-known and discovered multiple previously uncharacterized TF interactions within our detected set of TRMs. We further characterized the pairwise TRMs using long distance chromatin looping and motif co-occurrence data. We found that nearly all the TFs constituting TRMs detected by our approach interacted via chromatin looping, and that these TFs further interacted with promoters to influence TG expression through one of four possible regulatory configurations. Conclusion Here, we have provided a framework for detecting TRMs using neural network models containing multi-omics TF based regulatory features. We have also described these TRMs based on their regulatory potential along with presenting evidence for the possibility of TF interactions forming the TRMs occurring via chromatin looping.

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