ESTIMATION OF PAIN THRESHOLD FROM EEG SIGNALS OF SUBJECTS IN PHYSICAL THERAPY USING LONG-SHORT-TERM MEMORY DEEP LEARNING MODEL

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.

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Solar cycle prediction using a long short-term memory deep learning model

Research in Astronomy and Astrophysics ◽

10.1088/1674-4527/21/1/12 ◽

2021 ◽

Vol 21 (1) ◽

pp. 012

Author(s):

Qi-Jie Wang ◽

Jia-Chen Li ◽

Liang-Qi Guo

Keyword(s):

Deep Learning ◽

Solar Cycle ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Deep Learning Model

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2018.05.019 ◽

2018 ◽

Vol 99 ◽

pp. 24-37 ◽

Cited By ~ 93

Author(s):

Κostas Μ. Tsiouris ◽

Vasileios C. Pezoulas ◽

Michalis Zervakis ◽

Spiros Konitsiotis ◽

Dimitrios D. Koutsouris ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Epileptic Seizures ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Learning Network ◽

Long Short Term Memory ◽

Deep Learning Network

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CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419400050 ◽

2019 ◽

Vol 19 (01) ◽

pp. 1940005 ◽

Cited By ~ 3

Author(s):

ULAS BARAN BALOGLU ◽

ÖZAL YILDIRIM

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Algorithms ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Long Duration ◽

Proposed Model ◽

Clinical Scenarios ◽

Long Short Term Memory

Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6[Formula: see text]s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61[Formula: see text]Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.

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