scholarly journals Sleep Arousal Detection Using End-to-End Deep Learning Method Based on Multi-Physiological Signals

2018 ◽  
Author(s):  
Haoqi Li ◽  
Qineng Cao ◽  
Yizhou Zhong ◽  
Yun Pan
Keyword(s):  
Deep Learning ◽  
Physiological Signals ◽  
Learning Method ◽  
Sleep Arousal ◽  
End To End ◽  
Arousal Detection

scholarly journals Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3021 ◽  
Author(s):  
Wonju Seo ◽  
Namho Kim ◽  
Sehyeon Kim ◽  
Chanhee Lee ◽  
Sung-Min Park
Keyword(s):  
Deep Learning ◽  
Mental Stress ◽  
Detection Algorithm ◽  
Workplace Stress ◽  
Physiological Signals ◽  
Work Related ◽  
Average Accuracy ◽  
Conventional Machine ◽  
End To End

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.

Deep Learning Method for Risk Identification under Multiple Physiological Signals and PAD Model

2021 ◽  
pp. 104393
Author(s):  
Huizhe Ding ◽  
Raja Ariffin Raja Ghazilla ◽  
Ramesh Singh Kuldip Singh ◽  
Lina Wei
Keyword(s):  
Deep Learning ◽  
Risk Identification ◽  
Physiological Signals ◽  
Learning Method

scholarly journals Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

2019 ◽  
Vol 107 ◽  
pp. 109-117 ◽  
Author(s):  
Hyeonsoo Moon ◽  
Yuankai Huo ◽  
Richard G. Abramson ◽  
Richard Alan Peters ◽  
Albert Assad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Automated Pipeline ◽  
End To End ◽  
Spleen Segmentation

scholarly journals Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6535
Author(s):  
Maciej Dzieżyc ◽  
Martin Gjoreski ◽  
Przemysław Kazienko ◽  
Stanisław Saganowski ◽  
Matjaž Gams
Keyword(s):  
Deep Learning ◽  
Stress Test ◽  
Wearable Sensors ◽  
Physiological Signals ◽  
Trier Social Stress Test ◽  
Bayesian Optimization ◽  
Sensor Data ◽  
Affect Recognition ◽  
Affective Stimuli ◽  
End To End

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.

scholarly journals Corrigendum to “Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline” [Comput. Biol. Med. 107 (2019) 109–117]

2022 ◽  
Vol 140 ◽  
pp. 103684
Author(s):  
Hyeonsoo Moon ◽  
Yuankai Huo ◽  
Richard G. Abramson ◽  
Richard Alan Peters ◽  
Albert Assad ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Automated Pipeline ◽  
End To End ◽  
Spleen Segmentation

An end-to-end learning method for industrial defect detection

2019 ◽  
Vol 40 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Yupei Wu ◽  
Di Guo ◽  
Huaping Liu ◽  
Yao Huang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Network Architecture ◽  
Learning Method ◽  
Unified Framework ◽  
Convolutional Network ◽  
Data Set ◽  
Content Type ◽  
End To End ◽  
Dual Arm

Purpose Automatic defect detection is a fundamental and vital topic in the research field of industrial intelligence. In this work, the authors develop a more flexible deep learning method for the industrial defect detection. Design/methodology/approach The authors propose a unified framework for detecting defects in industrial products or planar surfaces based on an end-to-end learning strategy. A lightweight deep learning architecture for blade defect detection is specifically demonstrated. In addition, a blade defect data set is collected with the dual-arm image collection system. Findings Numerous experiments are conducted on the collected data set, and experimental results demonstrate that the proposed system can achieve satisfactory performance over other methods. Furthermore, the data equalization operation helps for a better defect detection result. Originality/value An end-to-end learning framework is established for defect detection. Although the adopted fully convolutional network has been extensively used for semantic segmentation in images, to the best knowledge of the authors, it has not been used for industrial defect detection. To remedy the difficulties of blade defect detection which has been analyzed above, the authors develop a new network architecture which integrates the residue learning to perform the efficient defect detection. A dual-arm data collection platform is constructed and extensive experimental validation are conducted.

scholarly journals End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1650
Author(s):  
Zhonglin Sun ◽  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Achilleas Sesis ◽  
Georgios Efstathopoulos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Learning Method ◽  
Neural Network Approach ◽  
Real Time System ◽  
Intentional Islanding ◽  
End To End ◽  
The Stability ◽  
Deep Learning Model ◽  
Load Generation

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.

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