Density map and fuzzy classification for breast density by using BI-RADS.

Mammographic density (MD) is conformed by a different percentage of stromal, epithelial, and adipose tissue within the breast. One of the most critical findings in mammographic patterns for establishing a diagnosis of breast cancer is high breast tissue density. There is a wide variety of works focused on the study and automatic calculation of general breast density; however, they do not provide more detailed information about the changes that may occur within the breast tissue. This work proposes to generate a breast density map based on a texture analysis to identify the internal composition and distribution of the breast tissue through the diffuse division technique of the different densities inside the breast. Therefore, it is possible to obtain a density map associated with the breast that allows us to distinguish and quantify the different types of breast densities and their distribution according to the Breast Imaging Reporting and Data System (BI-RADS Breast Density Category). The proposed methodology was tested with mammograms from the BCDR and InBreast databases, demonstrating consistency in results and reaching an accuracy of 84.2% and 81.3%, respectively. Finally, the information obtained from the density map and its analysis could be a support tool for the specialist physician to monitor changes in breast density over time, since the fuzzy classification carried out allows quantifying the degree of membership in the BI-RADS breast density classes.

On the use of Action Units and fuzzy explanatory models for facial expression recognition.

Facial expression recognition is related to the automatic identification of affective states of a subject by computational means. Facial expression recognition is used for many applications, such as security, human-computer interaction, driver safety, and health care. Although many works aim to tackle the problem of facial expression recognition, and the discriminative power may be acceptable, current solutions have limited explicative power, which is insufficient for certain applications, such as facial rehabilitation. Our aim is to alleviate the current limited explicative power by exploiting explainable fuzzy models over sequences of frontal face images. The proposed model uses appearance features to describe facial expressions in terms of facial movements, giving a detailed explanation of what movements are in the face, and why the model is making a decision. The model architecture was selected to keep the semantic meaning of the found facial movements. The proposed model can discriminate between the seven basic facial expressions, obtaining an average accuracy of 90.8±14%, with a maximum value of 92.9±28%.

Aplicación de la Sonificación de Señales Cerebrales en Clasificación Automática / Applying Brain Signals Sonification for Automatic Classification

En años recientes la sonificación de electroencefalogramas (EEG) ha sido utilizada como una alternativa para analizar señales cerebrales al convertir el EEG en audio. En este trabajo se aplica la sonificación a señales de EEG durante el habla imaginada o habla no pronunciada, con el objetivo de mejorar la clasificación automática de 5 palabras del idioma español. Para comprobarlo, se procesó la señal cerebral de 27 sujetos sanos. Estas señales fueron sonificadas para después extraer características con dos métodos diferentes: la transformada Wavelet discreta (DWT); y los coeficientes cepstrales en la escala de Mel (MFCC). Éste último comúnmente utilizado en tareas de reconocimiento de voz. Para clasificar las señales se aplicaron tres algoritmos distintos de clasificación Naive Bayes (NB), Máquina de vectores de soporte (SVM) y Random Forest (RF). Se obtuvieron resultados usando los 4 canales más cercanos a las áreas de lenguaje de Broca y Wernicke, así como también los 14 canales del dispositivo EEG utilizado. Los porcentajes de exactitud promedio para los 27 sujetos en los dos conjuntos de 4 y 14 canales usando sonificación de EEG fueron de 55.83% y 64.14% respectivamente, con lo que se logró mejorar ligeramente los porcentajes de clasificación de las palabras imaginadas respecto a no utilizar sonificación.

In recent years sonification of electroencephalograms (EEG) has been used as an alternative to analyze brain signals after converting EEG to audio. In this paper we applied the sonification to EEG signals during the imagined speech or unspoken speech, with the aim of improving the automatic classification of 5 words of Spanish. To check this, the brain signals of 27 healthy subjects were processed. These sonificated signals were processed to extract features with two different methods: discrete wavelet transform (DWT); and the Mel-frequencies cepstral coefficients (MFCC). The latter commonly used in speech recognition tasks. To classify the signals three different classification algorithms Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF) were applied. Results were obtained using the 4 channels closest to the language areas of Broca and Wernicke, as well as the 14 channels of the EEG device used. The percentages of average accuracy for the 27 subjects in the two sets of 4 and 14 channels using EEG sonification were 55.83% and 64.14% respectively, which are improvements in the classification rates of the imagined words compared with a scheme without sonification.

Análisis de señales electroencefalográficas para la clasificación de habla imaginada / Electroencephalographic Signals Analysis for Imagined Speech Classification

El presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: "arriba", "abajo", "izquierda", "derecha", y "seleccionar". Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11% y 47.93% usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20%, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.

This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (imagined or unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer, and consists of the Spanish language words: "arriba", "abajo", "izquierda", "derecha", and "seleccionar". To do this, we have recorded EEG signals from 27 subjects using a basic protocol to know a priori in what segments of the signal a subject imagines the pronunciation of the indicated word. Subsequently, discrete wavelet transform (DWT) is used to extract features from the segments. These are used to compute relative wavelet energy (RWE) in each of the levels in that EEG signal is decomposed and, it is selected a RWE values subset with the frequencies smaller than 32 Hz. Then, these are concatenated in two different configurations: 14 channels (full) and 4 channels (the channels nearest to the brain areas of Wernicke and Broca). The following three classifiers were trained using both configurations: Naive Bayes (NB), Random Forest (RF) and support vector machines (SVM). The best accuracies were obtained by RF whose averages were 60.11% and 47.93% using both configurations, respectively. Even though, the results are still preliminary, these are above 20%, this means they are more accurate than chance for five classes. Based on them, we can conjecture that the EEG signals could contain information needed for the classification of the imagined pronunciations of the words belonging to a reduced vocabulary.