Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Political Economy of Privatization with Corruption

In this paper by applying the Social Order Approach developed by North, Wallis and Weingast we indicate how natural states with limited access order and rent-oriented behaviors prevent achievement of privatization goals. This study applies descriptive-analytical approach to suggest that corruption in natural states is one of main limitations to success of privatization program and then investigates the impacts of corruption on three of the most important goals of privatization: strengthening the role of productive private sector in economy, promoting productivity and improving the public sector's financial health. Natural states with rent-oriented behaviors prevent strengthening of productive private sector through disturbing business environment and distorting transfers of ownership. Also corruption makes it difficult to access the goal of promoting productivity, through distorting the motivations of productive investment and reducing the available financial resources of productive private sector to improve productivity. Transferring state firms to the private sector and releasing of resources, makes an opportunity for natural states able to benefit elites more than before. Therefore, where institutional structure is corrupted and rent-based, it is impossible to improve financial health of public sector. Investigating the experience of Iran as a country with a natural state and widespread corruption confirms the theoretical-analytical framework of the study and shows that none of the goals of privatization has been achieved. During the implementation of privatization, not only the role of private sector has not improved, but also the share of government sector has increased. Also, total factor productivity index has not changed significantly and budget deficit of government has increased along with the acceleration of transfers of ownership

The Effect of Consumer’s Perceived Quality, Satiation and Satisfaction on Switching Intention (Case under study: Italian Restaurants in Tehran)

Maintaining customer interest to repurchase is an important issue in today's competitive market of Italian restaurants in Tehran. Therefore, determining the reasons that caused switching intention in customers, can be leading to arrange marketing purposes. So this research intends to evaluate effect of perceived quality, satiation and satisfaction on switching intention and to examine moderating role of involvement in relationship of satisfaction and switching intention. This study defined the population as all Italian restaurant customers who dined more than one time at a specific restaurant of Tehran in past month. Cluster random sampling was used and 391 questionnaires were collected. SEM was used for testing the direct assumptions and Interactive variable for moderating one. The results showed that perceived quality hasn’t considerable effect on switching intention; But has a negative effect on satiation and positive effect on satisfaction. Satiation and satisfaction have positive and negative impact on switching intention, respectively. Furthermore, involvement moderates the effect of satisfaction on switching, in a negative way

Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data

© 2015 IEEE. This study considers the prediction of driver's cognitive states from electroencephalographic (EEG) data. Extracting EEG features correlated with driver's cognitive states is key for achieving accurate prediction. However, high dimensionality and temporal-And-spatial correlations of EEG data make extraction of effective features difficult. This study explores the approaches based on deep belief networks (DBN) for feature extraction and dimension reduction. Experimental results of this study showed that DBN applied to channel epochs (DBN-C) produces the most discriminant features and the best classification performance is achieved when DBN-C is applied to the time-frequency and independent-component-Analysis transformed EEG data. The results suggested that DBN-C is a promising new method for extracting complex, discriminant features for EEG-based brain computer interfaces

EEG-based prediction of driver's cognitive performance by deep convolutional neural network

© 2016 Elsevier B.V. We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms