Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts.

Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this paper, we propose a support vector machine-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.

The reincorporation process of Colombian ex-combatants is hindered by their chronic exposure to violence, which affects their Emotional Processing (EP). Characterizing their EP will contribute to their reinsertion. The objective of this work is to define an EEG-based brain connectivity approach to identify differences in EP between Colombian ex-combatants and individuals who were not directly exposed to the armed conflict. The proposed approach involves defining the Regions of Interest (ROI) and selecting one of five commonly used brain connectivity metrics: Correlation, Cross-Correlation, Coherence, Imaginary part of Coherency, and Phase-Lag Index. Significant differences were found in the positive valence stimuli in the Beta frequency band. These results support the previously reported trend in the literature regarding the difficulties ex-combatants have to process emotional information with positive valence.

Category 3. This paper presents two resolution enhancement techniques applied to images of buried landmines acquired using a RASCAN radar, a holographic radar that is used mainly for sounding structural components of buildings, although it can be used to find buried objects too. The first technique is a phase-driven spatially variant regularization, and the second one is a wavelet-based image interpolation.

EEG recordings are often contaminated by environmental noise and physiological factors commonly known as artifacts, which affect the quality of the signal. This paper presents an automatic method to classify between artifactual and neural components in EEG signals using an Independent Component Analysis (ICA) and a Support Vector Machine. With the resultant model, we obtained a classification accuracy of 95.6% validating the model over real data. The proposed methodology outperforms other approaches, as it achieves similar accuracy using exclusively ICA components as features.