Classification of Cognitive Evoked Potentials for ADHD Detection in Children using Recurrence Plots and CNNs

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset condition characterized by difficulty paying attention and hyperactivity. The diagnosis of ADHD is made from psychological tests and electroencephalography (EEG). However, patient cooperation is necessary, which is a challenge with ADHD children. This work proposes a method for classification of ADHD and control cases from cognitive event-related potentials using recurrence plots and deep learning. A total of 44 children were included in this study (22 children with ADHD and 22 case controls). The signals were processed by a high-pass filter to eliminate DC components, wavelets transform with six decomposition levels, and synchronized averaging for each of the six channels (F3, AF3, F4, AF4, F7 and F8). Subsequently, the recurrence plot of each of the processed signals was obtained and used as inputs for two convolutional neural networks (CNN). The proposed models showed accuracies of 69.44% and 77,78%. © 2021 IEE

Desarrollo e implementación de un perfilómetro óptico por triangulación láser

Profilometers are instruments commonly used in surface metrology tasks. These instruments play a key role in the industry, such as in manufacturing and quality assurance. However, conventional profilometry techniques require direct contact with the object. In this work, we developed a practical and low-cost optical profilometer for contactless profilometry. The proposed profilometer is based on a laser triangulation system. A laser spot is projected onto the surface of an object, which is captured by a camera. The images are processed to detect the laser spot's location and obtain the object's surface height through calibration. We designed and built the translation stage to process the object's surface. Finally, encouraging results show that the profilometer provides high accuracy with fast acquisition. © 2020 IEEE

Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

The convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose a study of the effect of different speckle filtering methods on CNN performance. For the proposed metrics (Jaccard coefficient and BF-Score), it was obtained that the SRAD filter exhibited the best behavior even in the lowest quality data. In addition, the lowest values were obtained for the standard deviation and variance, which translates into lower data dispersion, better repeatability, and, therefore, greater confidence in its accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Editorial to the Special Issue on “Ocular Imaging for Eye Care”

The need for fast, inexpensive, and robust medical technology is now more crucial than ever [...