Rodrigo Salas

Rodrigo Salas
Universidad de Valparaíso (Chile) | CINV · Escuela de Ingeniería C. Biomédica

Dr. Ing.
Profesor Titular. Universidad de Valparaíso. Investigador Principal CINGS, iHealth.

About

116
Publications
23,553
Reads
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567
Citations
Introduction
I am currently investigating new methods and application for Artificial Neural Networks and Deep Learning. I am interested in enhancing current methods for processing biomedical signals and medical images.
Additional affiliations
September 2017 - present
Universidad de Valparaíso (Chile)
Position
  • Professor (Full)
November 2015 - present
Universidad de Valparaíso (Chile)
Position
  • Professor (Associate)
November 2013 - October 2015
Universidad de Valparaíso (Chile)
Position
  • Head of Department
Education
March 2004 - September 2010
Universidad Técnica Federico Santa María
Field of study
  • Machine Learning
March 2001 - August 2002
Universidad Técnica Federico Santa María
Field of study
  • Machine Learning and Artificial Intelligence
March 1996 - August 2002
Universidad Técnica Federico Santa María
Field of study
  • Computer Science

Publications

Publications (116)
Article
The aim of this study was to apply statistical parametric mapping (SPM) to compare temporal changes in EMG amplitude between rearfoot (RF) and forefoot (FF) running techniques. Eleven recreational runners ran on a treadmill at a self-selected speed, once using a RF strike pattern and once using a FF strike pattern (randomized order). The myoelectri...
Article
Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of...
Article
Full-text available
Introduction: In Chile, 1 in 8 pregnant women of middle socioeconomic level has gestational diabetes mellitus (GDM), and in general, 5–10% of women with GDM develop type 2 diabetes after giving birth. Recently, various technological tools have emerged to assist patients with GDM to meet glycemic goals and facilitate constant glucose monitoring, mak...
Article
Neuro-fuzzy models have been used to predict runoff from rainfall, a hydrological phenomenon associated with a degree of uncertainty. However, rainfall can be measured from different meteorological stations, and runoff forecasting can be biased. Thus, the aim of this work is to propose a new stacking neuro-fuzzy framework for predicting runoff from...
Article
Full-text available
Experts and international organizations hypothesize that the number of cases of fatal intimate partner violence against women increased during the COVID-19 pandemic, primarily due to social distancing strategies and the implementation of lockdowns to reduce the spread of the virus. We described cases of attempted femicide and femicide in Chile befo...
Article
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Linear functional analysis historically founded by Fourier and Legendre played a significant role to provide a unified vision of mathematical transformations between vector spaces. The possibility of extending this approach is explored when basis of vector spaces is built Tailored to the Problem Specificity (TPS) and not from the convenience or eff...
Preprint
The prediction of air pollutant levels plays an essential role in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter (PM). Even when elevated pollution episodes may be rare, they recirculate within an area where more people may present significant adverse health consequences. Thus, even when they are d...
Article
Full-text available
The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficient...
Article
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Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the ma...
Article
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Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winter...
Article
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The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventiona...
Article
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Recent advances in medical imaging have confirmed the presence of altered hemodynamics in bicuspid aortic valve (BAV) patients. Therefore, there is a need for new hemodynamic biomarkers to refine disease monitoring and improve patient risk stratification. This research aims to analyze and extract multiple correlation patterns of hemodynamic paramet...
Conference Paper
Online social networks are a powerful communication and information dissemination tool, particularly useful in complex scenarios such as social crises, natural disasters, and pandemics. However, one of the main problems, especially in socio-political crises, is the automatic detection of fake news. This problem is usually addressed with greater or...
Preprint
Full-text available
The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural net...
Preprint
Full-text available
The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural net...
Article
Full-text available
Resumen: Actualmente, el uso clínico-teórico de la electromiografía (EMG), basado en el comportamiento de los potenciales de acción registrados en el sistema musculoesquelético durante tareas funcionales, ha generado diversas áreas de conocimiento. Desde una perspectiva de investigación, los flujos de procesamientos vinculados a señales biomédicas...
Article
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Background Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with patients having normal valves...
Article
Full-text available
Medical image quality is crucial to obtaining reliable diagnostics. Most quality controls rely on routine tests using phantoms, which do not reflect closely the reality of images obtained on patients and do not reflect directly the quality perceived by radiologists. The purpose of this work is to develop a method that classifies the image quality p...
Article
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Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualizatio...
Preprint
Full-text available
Background: Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially implicated with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in comparison with normal valves, using a knowl...
Article
The handstand is an uncommon posture, highly demanding in terms of muscle and joint stability, used in sporting and artistic practices in a variety of disciplines. Despite its becoming increasingly widespread, there is no specific way to perform a handstand, and the neuromuscular organizational mechanisms involved are unknown. The objective of this...
Article
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Cryptocurrencies have been receiving the sustained attention of investors since 2009. These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms. Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by developing algorithm-based trading strategies. Such s...
Article
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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For t...
Conference Paper
Reducing and preventing road traffic accidents is a major public health problem and a priority for many nations. In this paper, we seek to explore the performance of explainable machine learning models applied to the prediction of road traffic crashes using a dataset containing nearly three million records of this type of events and the conditions...
Article
Modeling the relationship between rainfall and runoff is an important issue in hydrology, but it is a complicated task because both the high levels of complexity in which both processes are embedded and the associated uncertainty, affect the forecasting. Neuro-fuzzy models have emerged as a useful approach, given the ability of neural networks to o...
Conference Paper
Current public administration trends have raised the importance of putting the public values of the states' mission (social value and well-being) at the centre of the public management. Digital transformation of the states is a public initiative to adapt public processes of institutions to the new digital reality, and in this way, to adapt public p...
Article
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“Mercado Pzblico” is a Chilean electronic platform used for purchasing processes by Chilean public organizations for the last two decades. The main aim of this study is to characterize the Chilean public procurement ecosystem by using social network analysis to detect the main communities of suppliers based on who awarded the tenders. To do this, w...
Preprint
Full-text available
El presente reporte da cuenta del estudio realizado sobre la evolución de la cantidad de casos confirmados en Chile de COVID-19 hasta el 8 de Abril del 2020. Adicionalmente, se presenta la proyección sobre la cantidad de pacientes hospitalizados en UCI de acuerdo a los datos obtenidos del sitio oficial del Gobierno de Chile en el marco de su Plan d...
Preprint
Full-text available
El presente reporte da cuenta del estudio realizado sobre la evolución de la cantidad de casos confirmados en Chile de COVID-19. Adicionalmente, se presenta la proyección sobre la cantidad de pacientes hospitalizados en UCI de acuerdo a los datos obtenidos del sitio oficial del Gobierno de Chile en el marco de su Plan de Acción frente al coronaviru...
Article
The object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module....
Conference Paper
Full-text available
The prediction of cognitive tasks from electroencephalography (EEG) signals have allowed to discriminate the cognitive states emitted by the subjects and to carry out robust monitoring of cognition; a fact that is associated with the attention and performance of an individual's behavior, allowing greater control in the experiments. The objective of...
Conference Paper
Full-text available
Metacarpophalangeal deviations can be present from congenital diseases, nerve injuries, direct trauma such as fractures, autoimmune arthropathies such as that associated with lupus erythematosus (Jaccoud), to rheumatoid arthritis (RA), which is an inflammatory disease of joint components with varying degrees of destruction of the small joints of ha...
Conference Paper
Full-text available
Deep learning algorithms have recently been applied for image detection and classification, lately with good results in the medicine such as medical image analysis. This paper aims to support the detection of intracranial hemorrhage in computed tomography (CT) images using deep learning algorithms and convolutional neural networks (CNN). The motiva...
Article
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Resumen. Un importante porcentaje de las lesiones de miembros inferiores ha sido vinculado a la técnica de carrera, en particular, al contacto inicial con retropié (RP) o antepié (AP). Sin embargo, existe limitada evidencia de la actividad electromiográfica (EMG) para ambas condiciones. El objetivo de este estudio fue comparar la amplitud EMG en mi...
Article
Full-text available
It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That...
Article
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Purpose: Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampli...
Article
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Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generat...
Article
Full-text available
Based on similarity measures in the wavelet domain under a multichannel EEG setting, two new methods are developed for single-trial event-related potential (ERP) detection. The first method, named “multichannel EEG thresholding by similarity” (METS), simultaneously denoises all of the information recorded by the channels. The second approach, named...
Poster
Full-text available
The objective of this research is to group the scenarios and estimate the highest PM2.5 pollution in the "La Florida" area. For this, machine learning techniques will be used, such as Self-Organizing Maps (SOM) and Long short- term memory networks (LSTM)
Article
Functional Magnetic Resonance Imaging (fMRI) is a key neuroimaging technique. The classic fMRI analysis pipeline is based on the assumption that the haemodynamic response (HR) is the same across brain regions, time, and subjects. Although convenient, there is ample evidence that this assumption does not hold, and that these differences result in in...
Preprint
Full-text available
Deep learning models are part of the family of artificial neural networks and, as such, it suffers of catastrophic interference when they learn sequentially. In addition, most of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, in this article we propose the Self-Improving G...
Conference Paper
Full-text available
The present study showed a LSTM network for the prediction of specific muscle force during gait. Normally, simulation of muscle dynamics requires complex software that does not allow easy use and access to this technology. Furthermore, normally simulations use many human movement variables, which complexes the obtaining of the data. Thus, the prese...
Poster
Full-text available
Deep Learning approach, for predicting conditional intensity function, using deep feedforward neural networks and long short-term memory recurrent neural networks, from historic seismic data
Article
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We propose an efficient computational method to obtain the fractional derivative of a digital signal. e proposal consists of a new interpretation of the Grünwald-Letnikov differintegral operator where we have introduced a finite Cauchy convolution with the Grünwald-Letnikov dynamic kernel. e method can be applied to any signal without knowing its a...
Article
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Service-based systems (SBSs) need to be reconfigured when there is evidence that the selected Web services configurations no further satisfy the specifications models and, thus the decision-related models will need to be updated accordingly. However, such updates need to be performed at the right pace. On the one hand, if the updates are not quickl...
Article
Full-text available
Useful knowledge acquisition from known and systematized information (data) is a big challenge for researchers, users and finally, decision makers. In this sense, knowledge discovery from data (KDD) process represents a valuable tool for information analysis. Moreover, this work presents an approach through KDD in time series pattern identification...
Conference Paper
Full-text available
Deep Convolutional Neural Networks, like most connectionist models, suffers from catastrophic forgetting while training for a new, unknown task. One of the simplest solutions to this issue is adding samples of previous data, with the drawback of increasingly having to store training data; or generating patterns that evoke similar responses of the p...