Publications by authors named "Alvaro A Orozco"

We deal with an important problem in the field of anesthesiology known as automatic segmentation of nerve structures depicted in ultrasound images. This is important to aid the experts in anesthesiology, in order to carry out Peripheral Nerve Blocking (PNB). Ultrasound imaging has gained recent interest for performing PNB procedures since it offers a non-invasive visualization of the nerve and the anatomical structures around it.

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Article Synopsis
  • - Second order diffusion tensor (DT) fields are crucial in clinical fields like brain fiber mapping and diagnosing neuro-degenerative diseases, but low spatial resolution in MRI leads to challenges in accurately capturing tissue structures.
  • - The study introduces a novel feature-based interpolation method using multi-output Gaussian processes (MOGP) to enhance the spatial resolution of DT fields by treating eigenvalues and Euler angles of diffusion tensors as separate but connected outputs.
  • - This MOGP method outperforms existing techniques for DT interpolation in terms of accuracy and effectively maintains important characteristics of diffusion tensors, showing performance comparable to advanced methods like Generalized Wishart processes.
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Affective computing systems has a great potential in applications for biofeedback systems and cognitive conductual therapies. Here, by analyzing the physiological behavior of a given subject, we can infer the affective state of an emotional process. Since, emotions can be modeled as dynamic manifestations of these signals, a continuous analysis in the valence/arousal space, brings more information of the affective state related to an emotional process.

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Approaches to evaluate voice quality include perceptual analysis, and acoustical analysis. Perceptual analysis is subjective and depends mostly on the ability of a specialist to assess a pathology, whereas acoustical analysis is objective, but highly relies on the quality of the so called annotations that the specialist assigns to the voice signal. The quality of the annotations for acoustical analysis depends heavily on the expertise and knowledge of the specialist.

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Several cases related to chronic pain, due to accidents, illness or surgical interventions, depend on anesthesiology procedures. These procedures are assisted with ultrasound images. Although, the ultrasound images are a useful instrument in order to guide the specialist in anesthesiology, the lack of intelligibility due to speckle noise, makes the clinical intervention a difficult task.

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Peripheral Nerve Blocking (PNB), is a procedure used for performing regional anesthesia, that comprises the administration of anesthetic in the proximity of a nerve. Several techniques have been used with the purpose of locating nerve structures when the PNB procedure is performed: anatomical surface landmarks, elicitation of paresthesia, nerve stimulation and ultrasound imaging. Among those, ultrasound imaging has gained great attention because it is not invasive and offers an accurate location of the nerve and the structures around it.

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Article Synopsis
  • * The study demonstrates that analyzing microelectrode recording (MER) signals with sparse representation techniques improves the accuracy of STN identification.
  • * Three methods—Method of Frames (MOF), Best Orthogonal Basis (BOB), and Basis Pursuit (BP)—show superior performance compared to traditional signal processing methods, achieving over 98% classification accuracy in tests.
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In the embryo development problem for the Drosophila melanogaster, a set of molecules known as mor-phogens are responsible for the embryo segmentation. These morphogens are encoded by different genes, including the GAP genes, maternal coordination genes and pair-rule genes. One of the maternal coordination genes encodes the Bicoid morphogen, which is the responsible for the development of the Drosophila embryo at the anterior part and for the control and regulation of the GAP genes in segmentation of the early development of the Drosophila melanogaster.

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Emotion recognition is a challenging research problem with a significant scientific interest. Most of the emotion assessment studies have focused on the analysis of facial expressions. Recently, it has been shown that the simultaneous use of several biosignals taken from the patient may improve the classification accuracy.

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  • This paper discusses a method for automatically identifying biomedical signals, specifically Microelectrode Recordings (MER) and Electrocardiography (ECG) signals, using unsupervised learning techniques.
  • The approach combines classic and Bayesian estimation theories, employing Gaussian mixture models with two estimation methods: the Expectation-Maximization (EM) algorithm and variational inference.
  • The results demonstrate an accuracy of over 85% for MER and 90% for ECG in classifying these signals, outperforming traditional classifiers like Naive Bayes and K-nearest neighbor.
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Emotional behavior is an active area of study in the fields of neuroscience and affective computing. This field has the fundamental role of emotion recognition in the maintenance of physical and mental health. Valence/Arousal levels are two orthogonal, independent dimensions of any emotional stimulus and allows an analysis framework in affective research.

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Article Synopsis
  • * Recent efforts are focusing on developing automatic systems that use supervised learning to accurately localize brain regions across different patients.
  • * This study demonstrates that utilizing multi-task learning to share information between patients enhances accuracy in targeting the Subthalamic Nucleus, outperforming traditional methods in real datasets.
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  • The paper introduces NEUROZONE, a software system designed to assist in the precise positioning of microelectrodes during Deep Brain Stimulation surgeries by analyzing microelectrode recordings (MER) for real-time brain structure recognition.
  • NEUROZONE includes features for offline database processing and classifier training, enhancing the automatic identification of brain target areas while aiding medical specialists in reducing potential side effects from misidentification.
  • The software has been successfully tested at the Institute for Epilepsy and Parkinson of the Eje Cafetero in Colombia, achieving over 85% accuracy in identifying the Subthalamic Nucleus using a naive Bayes classifier.
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