Browsing by Author "Wong de Balzan, Sara"

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Algoritmos de aprendizaje no supervisado para la estimación de la resistencia a la insulina y el síndrome metabólico en el adulto mayor de la ciudad de Cuenca
(2017) Vintimilla García, Christian Xavier; Wong de Balzan, Sara; Astudillo Salinas, Darwin Fabián
In this degree work, Insulin Resistance (IR) and Metabolic Syndrome (MS) are explored in the Cuenca‘s elderly population, from the point of view of unsupervised classification methods. In the case of IR, five estimation methods were analyzed using a K-means classification on a population of 119 people older than 65 years old who underwent a two-point Oral Glucose Tolerance Test (OGTT). The K-means algorithm with K = 2 and K = 3 was applied in onedimensional experiments for the Homa-IR, Quicki, Avignon, and Matsuda methods. The results obtained allowed the development of a platform to aid in the diagnosis of IR. These findings were object of two publications (IV Congress of Information and Communication Technologies TIC-EC 2017, and IEEE ETCM 2017: 2nd IEEE Ecuador Technical Chapters Meeting). For the study of MS using Kohonen’s SOM, two types of normalization (binary and by rank) were analyzed for the inputs of the neural network using a population of 387 elderly people. The results, using a pre-processing by ranges allow a better classification of the population in all cases. This study allowed to select the type of pre-processing for the diagnosis of MS in the elderly population of the city of Cuenca using SOM and was the object of a publication in the V Congress REDU 2017 and the II Congress I+D+Ingeniería. The future work is oriented to validate the results obtained in other elderly populations.
Desarrollo y validación de algoritmos para el estudio de la variabilidad de la frecuencia cardíaca en adultos mayores
(2015) Parra Orellana, Freddy Andres; Wong de Balzan, Sara; Palacio Baus, Kenneth Samuel
This work is part of the project: Heart Rate Variability (HRV) and Insulin Sensibility among the Senior Population of Cuenca (Variabilidad de la Frecuencia Cardiaca (VFC) y Sensibilidad a la Insulina en la Población Adulta Mayor de Cuenca), funded and promoted by the Prometo Program and the Research Direction of the University of Cuenca (Dirección de Investigación de la Universidad de Cuenca – DIUC). The main goal of this thesis was to develop algorithms aimed to study the Heart Rate Variability. The tasks performed in this work include: i) the acquisition of SCP-ECG electrocardiographic registers from a senior population set, ii) the development of a platform aimed to integrate ECG registers with clinic and demographic information of studied subjects; iii) ECG processing by making use of the Pan-Tompkins QRS detector and artifact filtering in order to obtain RR series, and iv) the application of non-parametric methods (PSD and Hamming) and parametric methods (Burg) for temporal and spectral estimation, in addition to temporal parameter (SDRR, SMRRD) computation and frequency parameter (LF,HF, LF/HF) computation from the HRV. The development of this project makes use of a Graphic User Interface (GUI) called UCUENCAViewer ANALISIS VFC, which will allow researchers to analyze senior population’s HRV. Additionally, this tool will be available for teaching purposes and able to be used in future research.
Implementación de un sistema de anotación y evaluación de los detectores de QRS: PAN&TOMPKINS, basado en la transformada de ondaletas sobre electrocardiogramas de esfuerzo
(2016) Fajardo Reinoso, Juan Manuel; Wong de Balzan, Sara; Astudillo Salinas, Darwin Fabián
The aim of this project is to develop a semiautomatic annotation RR time-series system using an eight leads stress database named DICARDIA. In a first stage the user realizes a visual inspection validating or denying the channel proposed by the system based in statistics measure. In the second stage the system has two options based in the quality of RR-time series, if the series contains few artifacts the annotations is realized using interval of annotations, in the case of noisy series, the system allows annotations beat by beat. Finally, an annotation file is generated under standard formats that are used to evaluate QRS detectors. The performance measures were the Sensibility (Se) and positive predictive value (P+). The results realized over 255432 beats provide a Se of 97.347630% and P+ of 96.84413% for the Pan & Tompkins (PT) and Se of 95.540104% and P+ of 93.0337086% for based wavelet transform (WT). The procedure developed in this work can be seen as a valuable starting point in semiautomatic annotation of large electrocardiographic databases, as well to evaluate and to improve stress ECG delineations and provides of delineations to evaluate the performance measure of QRS detectors. This work has been published in two indexed journals. The semiautomatic annotation system will be presented and published in TIC.EC 2016 memories and the evaluation of QRS detection algorithm will be published in AndesCon 2016 memories.
Umbral de detección de la intención de movimiento de los músculos involucrados en la flexo-extensión de la rodilla durante la marcha
(2018-05-07) Farfán Bernal, Angel Bolívar; Rojas Bustos, Andrés Bolívar; Wong de Balzan, Sara; Minchala Ávila, Luis Ismael
Three motion intention detectors of the muscles involved in flexion-extension of the knee during gait were studied, based on surface electromyography (sEMG). The purpose of this study is to estimate the detection threshold of the movement intention of nine specific muscles. We used a sEMG acquisition system formed by an EKG / EMG shield based on Arduino Mega. Participants were 21 subjects (14 men), age (21.52 ± 2.4 years) without pathologies at the level of the knee. sEMG of 9 muscles were recorded at 1 KHz and 58 ± 3 seconds in duration. Subsequently, a study of three detectors was carried out: Single Threshold (D1), Double Threshold (D2) and Double Statistical Threshold (D3). The comparison was made based on the distance to the perfect detection point (DDP) and time delay (Td), using a cross-validation technique. D3 presented a better performance (SEN: 85.88 ± 2.18%, ESP: 86.11 ± 3.55% Td: 6.24 ± 2.42 ms). Additionally, the minimum / desirable value of the SNR was determined for a reliable measurement of the Medium Frequency (MNF) of the sEMG. The signals were contaminated with different noise levels. The thresholds were determined using the K-means algorithm (Minimum: 5.51 dB, Desirable 12.28 dB). The beginning of muscle contraction oversees activating an orthopedic device, therefore, an accurate detection of this episode of muscular contraction is of crucial importance to ensure the proper operation of the device. The D3 detector fulfills these characteristics and this finding constitutes a contribution to the development of the exoskeleton prototype proposed by the DEET.