Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning

2016 ◽  
pp. 802-810 ◽  
Author(s):  
Qin Lin ◽  
Shu-qun Ye ◽  
Xiu-mei Huang ◽  
Si-you Li ◽  
Mei-zhen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Eeg Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

scholarly journals Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

2021 ◽  
Author(s):  
Gracielly G. F. Coutinho ◽  
Gabriel B. M. Câmara ◽  
Raquel de M. Barbosa ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Deep Learning ◽  
Viral Genome ◽  
Genomic Sequence ◽  
Confusion Matrix ◽  
Taxonomic Classification ◽  
Classification Problems ◽  
Virus Identification ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

Automatic classification of EEG signals via deep learning

2021 ◽  
Author(s):  
Tao Wu ◽  
Xiangzeng Kong ◽  
Yiwen Wang ◽  
Xue Yang ◽  
Jingxuan Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Classification ◽  
Eeg Signals

Classification of productive profile using brain signals: Deep learning and neuroscience meets the mining industry

2021 ◽  
Author(s):  
Ana Siravenha ◽  
Walisson Gomes ◽  
Renan Tourinho ◽  
Sergio Viademonte ◽  
Bruno Gomes
Keyword(s):  
Deep Learning ◽  
Iron Ore ◽  
Signal To Noise Ratio ◽  
Mining Industry ◽  
Eeg Signals ◽  
Neural Data ◽  
Technical Staff ◽  
Brain Signals ◽  
Mining Complex

Classification of electroencephalography (EEG) signals is a complex task. EEG is a non-stationary time process with low signal to noise ratio. Among many methods usedfor EEG classification, those based on Deep Learning (DL) have been relatively successful in providing high classification accuracies. In the present study we aimed at classify resting state EEGs measured from workers of a mining complex. Just after the EEG has been collected, the workers undergonetraining in a 4D virtual reality simulator that emulates the iron ore excavation from which parameters related to their performance were analyzed by the technical staff who classified the workers into four groups based on their productivity. Twoconvolutional neural networks (ConvNets) were then used to classify the workers EEG bases on the same productivity label provided by the technical staff. The neural data was used in three configurations in order to evaluate the amount of datarequired for a high accuracy classification. Isolated, the channel T5 achieved 83% of accuracy, the subtraction of channels P3 and Pz achieved 99% and using all channels simultaneously was 99.40% assertive. This study provides results that add to the recent literature showing that even simple DL architectures are able to handle complex time series such as the EEG. In addition, it pin points an application in industry with vast possibilities of expansion.

scholarly journals Classification of Alcoholic EEG Signals Using a Deep Learning Method

2020 ◽  
pp. 1-1
Author(s):  
Leila Farsi ◽  
Siuly Siuly ◽  
Enamul Kabir ◽  
Hua Wang
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Eeg Signals

scholarly journals Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Liu ◽  
Lianfeng Li ◽  
Jian Ma
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Short Time Fourier Transform ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

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