Since the last century, we have witnessed a steady evolution of advertising techniques in an effort to adapt to the new social context in the market. As a strategic resource, Neuroscience brings a new perspective by allowing you to explore those difficult or verbally unconscious motives behind consumer behaviours. The present work aims to discover the relationship between the emotions induced in audiovisual advertising messages and their impact on the memory of the subjects. To achieve this goal, an experiment was carried out with eight audiovisual advertising messages (six representatives of the basic emotions: joy, surprise, anger, disgust, fear and sadness, and two rational ones that show the technical specifications of the product). Neuromarketing techniques such as the electrical activity of the heart (ECG) and the electrodermal activity (EDA) of the subjects are used, on one hand; and, on the other, a conventional research technique, a questionnaire applied to the subjects that participated in the research. The results show variations in the measures performed in the commercials corresponding to joy, surprise and anger, while for both, remembrance of the message transmitted and activity of the advertiser, the commercial with the best results has been the one regarding sadness, advertisement that has also been considered the most attractive for participating subjects. Desde el siglo pasado hemos presenciado una evolución constante de las técnicas de comunicación publicitarias en un intento de adaptación a las nuevas realidades sociales del mercado. Como recurso estratégico, la Neurociencia aporta una nueva perspectiva al permitir explorar aquellos motivos difíciles de verbalizar o inconscientes que hay detrás de los comportamientos de los consumidores. El presente trabajo tiene como objetivo descubrir la relación entre las emociones inducidas en los mensajes publicitarios audiovisuales y su impacto en el recuerdo de los sujetos. Para alcanzar este objetivo se ha realizado un experimento con ocho mensajes publicitarios audiovisuales (seis representativos de seis emociones básicas: alegría, sorpresa, ira, asco, miedo y tristeza; y dos racionales) en el que se han utilizado, por un lado, técnicas de Neuromarketing como son la actividad eléctrica cardíaca (ECG) y la actividad eléctrica de la dermis (AED) de los sujetos; y, por otro, una técnica de investigación convencional, un cuestionario aplicado a los sujetos que han participado en la investigación. Los resultados ponen de manifiesto variaciones en las medidas realizadas en los mensajes correspondientes a la alegría, la sorpresa y la ira, mientras que, tanto para el recuerdo sugerido del mensaje trasmitido como para la actividad del anunciante, el anuncio con mejores resultados ha sido el de la tristeza, anuncio que también ha sido considerado el más atractivo para los sujetos participantes.

The goal of this paper is to determine whether the photoplethysmography (PPG) can replace the ECG-based detection of heart rate turbulence. Using the PPG, classification of ventricular premature beats (VPBs) is accomplished with a linear classifier. The two conventional parameters turbulence onset and slope are studied together with a recently introduced parameter characterizing turbulence shape. Performance is studied on a dataset with 4131 VPBs, recorded from a total of 27 patients in different clinical contexts (hemodialysis treatment, intensive care monitoring, and electrophysiological study). The sensitivity/specificity of VPB classification was found to be 90.5/99.9%, with an accuracy of 99.3%, suggesting that classification of VPBs can be reliable made from the PPG. The main difference between the two types of turbulence analysis stems from the fact that the pulse transit time varies largely immediately after the VPB. Out of the 22 patients which had a sufficient number of VPBs, the outcome of the ECG- and PPG-based analysis was identical in 21. It is concluded that the PPG may serve as a surrogate technique for the ECG in turbulence analysis.

Great effort has been devoted in recent years to the development of sudden cardiac risk predictors as a function of electric cardiac signals, mainly obtained from the electrocardiogram (ECG) analysis. But these prediction techniques are still seldom used in clinical practice, partly due to its limited diagnostic accuracy and to the lack of consensus about the appropriate computational signal processing implementation. This paper addresses a three-fold approach, based on ECG indexes, to structure this review on sudden cardiac risk stratification. First, throughout the computational techniques that had been widely proposed for obtaining these indexes in technical literature. Second, over the scientific evidence, that although is supported by observational clinical studies, they are not always representative enough. And third, via the limited technology transfer of academy-accepted algorithms, requiring further meditation for future systems. We focus on three families of ECG derived indexes which are tackled from the aforementioned viewpoints, namely, heart rate turbulence, heart rate variability, and T-wave alternans. In terms of computational algorithms, we still need clearer scientific evidence, standardizing, and benchmarking, siting on advanced algorithms applied over large and representative datasets. New scenarios like electronic health recordings, big data, long-term monitoring, and cloud databases, will eventually open new frameworks to foresee suitable new paradigms in the near future.

Fifth-generation (5G) requires a highly accurate estimate of the channel state information (CSI) to exploit the benefits of massive multiple-input-multiple-output (MaMIMO) systems. 5G systems use pilot sequences to estimate channel behaviour using traditional methods like least squares (LS), or minimum mean square error (MMSE) estimation. However, traditional methods do not always obtain reliable estimations: LS exhibits a poor estimation when inadequate channel conditions (i.e., low- signal-to-noise ratio (SNR) region) and MMSE requires prior statistical knowledge of the channel and noise (complex to implement in practice). We present a deep learning framework based on deep neural networks (DNNs) for fifth-generation (5G) MaMIMO channel estimation. After a first preliminary scheme with which we verify the good estimation capacity of our DNN-based approach, we propose two different models, which differ in the information processed by the DNN and benefit from lower computational complexity or greater flexibility for any reference signal pattern, respectively. The results show that, compared to the LS-based channel estimation, the DNN approach decreases the mean square error (MSE) and the system’s spectral efficiency (SE) increases, especially in the low- SNR region. Our approach provides results close to optimal MMSE estimation but benefits from not requiring any prior channel statistics information.

Fast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records. This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers. The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.