44 research outputs found
Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps
No full textThis study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems
Análisis de señales acústicas de pulmón basado en coeficientes cepstrales de la escala Mel y mapas auto-organizados
No full textThis study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems.En este trabajo se realizó un análisis de anormalidades en señales acústicas de pulmón. La metodología incluyó el uso de coeficientes cepstrales de la escala Mel (MFCC), Mapas Auto-Organizados (SOM) y el algoritmo de agrupamiento K-means. Los modelos obtenidos con los mapas son conocidos como redes neuronales artificiales, que pueden ser entrenados en una forma supervisada o no supervisada. Ambos tipos de entrenamiento fueron usados para comparar el uso de este tipo de herramientas computacionales en estudios de señales respiratorias. Los resultados mostraron un 85 % de acierto en la clasificación, cuando fue implementado un entrenamiento supervisado. Al realizar tareas de agrupamiento con entrenamiento no supervisado fue encontrado que el número de grupos más adecuado es de tres. En general, los modelos SOM pueden ser usados en este tipo de señales como una estrategia útil en sistemas de diagnóstico, encontrando información en los datos y realizando clasificación para sistemas de apoyo a decisión
On the Use of Neuroevolutive Methods as Support Tools for Diagnosing Appendicitis and Tuberculosis
No full textOn the use of neuroevolutive methods as support tools for diagnosing appendicitis and tuberculosis
No full textArtificial neural networks are being used in diagnosis support systems
to detect different kind of diseases. As the design of multilayer perceptron
is an open question, the present work shows a comparison between a traditional
empirical way and neuroevolution method to find the best architecture to solve
the disease detection problem. Tuberculosis and appendicitis databases were
employed to test both proposals. Results show that neuroevolution offers a good
alternative for the tuberculosis problem but there is lacks of performance in the
appendicitis one.http://unidadinvestigacion.usta.edu.c
Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning
No full textElectroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model’s performance depends upon sex and is limited when multiple classes are included in machine learning modeling
EDA-graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection
No full textThe continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (NoC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, NoC, diameter, and radius demonstrated significant differences (p<0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68. </p
Modelo de negocio circular para las empresas del sector textil del Valle del Cauca, Colombia
No full textilustraciones y gráficos a colorDoctoradoDoctor en Administración de NegociosTrabajo de grado - Doctorado en Administración de Negocios. El autor del trabajo no autoriza consulta, préstamo ni descarga.Sedes::Cali::Línea de investigación empresa, territorio y desarrollo sostenible (Cali
Derechos humanos y políticas públicas del Estado colombiano en materia de nutrición de los niños Wayuu
No full textArtículo de InvestigaciónEl trabajo comprende la situación alimentaria de los niños de la etnia wayuu, debido a los elevados índices de desnutrición crónica y aguda, y a las tasas de mortalidad asociadas a este problema. Dicha situación ha llamado la atención de organismos internacionales en relación al cumplimiento del derecho a la alimentación y seguridad alimentaria consagrado en la Constitución política de Colombia.PregradoAbogadoINTRODUCCIÓN
1. PLANTEAMIENTO DEL PROBLEMA: PRODUCCIÓN ALIMENTARIA DEL PUEBLO WAYUU.
2. MARCO REFERENCIAL Y CATEGORÍAS 3. CONCLUSIONES. BIBLIOGRAFÍ
Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths
No full textPURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. METHODS: Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. RESULTS: Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. CONCLUSION: The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses
