RESUMEN
BACKGROUND: Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. METHOD: In this study, a method based on the Convolutional Neural Network (CNN) approach is proposed to assess stress induced by the Montreal Imaging Stress Task. The proposed model is trained on the heart rate signal derived from functional Near-Infrared Spectroscopy (fNIRS), which is referred to as HRF. In this regard, fNIRS signals of 20 healthy volunteers were recorded using a configuration of 23 channels located on the prefrontal cortex. The proposed deep learning system consists of two main parts where in the first part, the one-dimensional convolutional neural network is employed to build informative activation maps, and then in the second part, a stack of deep fully connected layers is used to predict the stress existence probability. Thereafter, the employed CNN method is compared with the Dense Neural Network, Support Vector Machine, and Random Forest regarding various classification metrics. RESULTS: Results clearly showed the superiority of CNN over all other methods. Additionally, the trained HRF model significantly outperforms the model trained on the filtered fNIRS signals, where the HRF model could achieve 98.69 ± 0.45% accuracy, which is 10.09% greater than the accuracy obtained by the fNIRS model. CONCLUSIONS: Employment of the proposed deep learning system trained on the HRF measurements leads to higher stress classification accuracy than the accuracy reported in the existing studies where the same experimental procedure has been done. Besides, the proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time monitoring of stress assessment.
Asunto(s)
Redes Neurales de la Computación , Espectroscopía Infrarroja Corta , Encéfalo/diagnóstico por imagen , Frecuencia Cardíaca , HumanosRESUMEN
Hemangioma is one of the most common benign congenital complications of the human body which can arise in interior organs and external limbs. The main aim of this work is to present a new method for automatic detection of liver hemangioma and its boundaries in ultrasound images, using image processing techniques. Overall there are two phases, the preprocessing procedure and the boundary delineation phase. The preprocessing phase includes three main stages: 1. Image contrast enhancement using Difference of Offset Gaussian (DoOG) method, 2. Applying Canny edge filtering, 3. Applying an adaptive threshold in order to detect the ROI (hemangioma). Following, the snake algorithm is used to segment the hemangioma region in the second phase. For the quantitative assessment of the proposed method for the segmentation stage, the results derived via the proposed algorithms have been compared with the corresponding segmented regions determined by an expert using three similarity criteria. The results showed 73 percent similarity without pre-processing and 90 percent similarity with pre-processing.
Asunto(s)
Hemangioma/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Neoplasias Hepáticas/diagnóstico por imagen , Algoritmos , Automatización , Humanos , UltrasonografíaRESUMEN
In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.