Diseño de una cámara Térmica IR de bajo coste basada en microcontrolador

[ES] En el presente Proyecto Final de Carrera se ha desarrollado la implementación, integración y soporte para una cámara térmica, que puede ser de utilidad en áreas donde se requiera la inspección térmica de superficies. Para su desarrollo, en primer lugar, se ha desarrollado el código de programación para conseguir comunicar un sensor IR, con una pantalla LCD a través de una tarjeta de programación Arduino Mega 2650 rev3. La finalidad ha sido lograr proporcionar al usuario de la misma una interfaz interactiva, así como el desarrollo de aplicaciones que puedan ser útiles durante el uso de la misma. Así, se ha desarrollado una barra térmica, un analizador de imagen y la capacidad de almacenamiento de datos en una tarjeta microSD. En segundo lugar, se ha implementado un sistema de alimentación autónomo con la finalidad de dar soporte energético integrado a la cámara térmica. Para su realización se ha hecho uso de una batería de Ion-Litio, un convertidor Boost y un sistema BMS. Los objetivos han sido dotar de autonomía al proyecto mediante un sistema recargable de energía. En tercer lugar, se ha desarrollado el modelado de la carcasa que servirá de soporte a todos los componentes, logrando formar un único sistema mediante el cual el usuario no tenga interacción con los componentes que la conforman, a la vez de dar protección al conjunto. Para su modelado físico se ha realizado su impresión 3D. Finalmente, notar que pretende ser una alternativa económica ante las diferentes ofertas que oferta el mercado.[EN] In this Final Career Project has been developed the implementation, integration and support for a thermal camera. This camera can use for studying thermal surfaces. Firstly, programming code has been developed aiming to communicate the thermal camera with LCD screen, through Arduino Mega 2650 rev3 programming board. The purpose of this is to get an interactive interface and to create applications which may be useful for the camera management. For that, has been created a thermal bar, a picture analyser and skill to save information in a microSD card. Secondly, it has been implemented an autonomous feed system to give energetic support to the camera. It needed an ion-lithium battery, a Boost converter and a BMS system. In fact, the target of this goal has been given autonomy to the project thought a recharging energy system. Thirdly, a model of case has been created which can use to support all components, getting an only and isolated system. The thermal camera has been made with 3D system. Finally, this thermic camera can be an economy alternative among different offers which It¿s in the market.Nieto Del Amor, F. (2018). Diseño de una cámara Térmica IR de bajo coste basada en microcontrolador. http://hdl.handle.net/10251/107689TFG

Estudio comparativo de las diferentes medidas de entropía para la predicción del parto prematuro

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%.[CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%.[EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.Nieto Del Amor, F. (2020). Estudio comparativo de las diferentes medidas de entropía para la predicción del parto prematuro. Universitat Politècnica de València. http://hdl.handle.net/10251/161660TFG

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

[EN] Although preterm labor is a major cause of neonatal death and often leaves health sequels in the survivors, there are no accurate and reliable clinical tools for preterm labor prediction. The Electrohysterogram (EHG) has arisen as a promising alternative that provides relevant information on uterine activity that could be useful in predicting preterm labor. In this work, we optimized and assessed the performance of the Dispersion Entropy (DispEn) metric and compared it to conventional Sample Entropy (SampEn) in EHG recordings to discriminate term from preterm deliveries. For this, we used the two public databases TPEHG and TPEHGT DS of EHG recordings collected from women during regular checkups. The 10th, 50th and 90th percentiles of entropy metrics were computed on whole (WBW) and fast wave high (FWH) EHG bandwidths, sweeping the DispEn and SampEn internal parameters to optimize term/preterm discrimination. The results revealed that for both the FWH and WBW bandwidths the best separability was reached when computing the 10th percentile, achieving a p-value (0.00007) for DispEn in FWH, c = 7 and m = 2, associated with lower complexity preterm deliveries, indicating that DispEn is a promising parameter for preterm labor prediction.This work was supported by the Spanish ministry of economy and competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; González Martínez, M.; Monfort-Ortiz, R.; Prats-Boluda, G. (2021). Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination. SCITEPRESS. 260-267. https://doi.org/10.5220/0010316602600267S26026

Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity

[EN] The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG¿related studies have been found in the literature. We thus aimed to identify EHG¿biomarkers to predict IOL success (active phase of labour in¿¿¿24¿h) and determine the influence of the myoelectrical response on the parity of this group. Statistically significant and sustained differences between the successful and failed groups were found from 150¿min in amplitude and non¿linear parameters, especially in Spectral Entropy and in their progression rates. In the nulliparous¿parous comparison, parous women showed statistically significantly higher amplitude progression rate. These biomarkers would therefore be useful for early detection of the risk of induction failure and would help to develop more robust and generalizable IOL success¿prediction systems.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR and PID2021-124038OB-I00). Funding for open access charge: CRUE-Universitat Politècnica de ValènciaDiaz-Martinez, A.; Monfort-Ortiz, R.; Ye Lin, Y.; Garcia-Casado, J.; Nieto-Tous, M.; Nieto Del-Amor, F.; Diago-Almela, VJ.... (2023). Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybernetics and Biomedical Engineering (Online). 43(1):142-156. https://doi.org/10.1016/j.bbe.2022.12.00414215643

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

[EN] Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and by the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Prats-Boluda, G.; Martínez-De-Juan, JL.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.; Ye Lin, Y. (2021). Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. Sensors. 21(10):1-15. https://doi.org/10.3390/s21103350S115211

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR), and by the Generalitat Valenciana (AICO/2019/220)Nieto Del-Amor, F.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211

Comparative Study of Uterine Myoelectrical Response to Labour Induction Drugs in Nulliparous and Parous Women with Different EHG Analysis Techniques

[EN] Induction of labour (IOL) is one of the most widespread practices to promote the onset of labour when maternal-fetal well-being is compromised. Currently, the monitoring of this procedure in clinical practice is performed with subjective and poorly reproducible techniques such as tocography and cervical assessment, without taking into account other obstetric variables of special relevance as parity. Electrohysterography (EHG) has emerged as a promising alternative due to its usefulness and non-invasiveness. Traditionally, EHG has been characterized by analyzing the EHG-bursts (EBA) associated with uterine contractions and computing temporal, spectral and nonlinear parameters. Recent studies characterize the EHG by considering both EHG-burst and basal activity (WEWA). The first objective of this study was to discern which analysis technique presented the best performance for EHG characterization during IOL. Subsequently, differences in uterine myoelectric response to IOL drugs in nulliparous and parous women were analyzed and compared. EHG recordings were performed during the first 4 hours of IOL in 15 nulliparous and 10 parous women. EBA results showed a greater number of parameters with significant differences with their corresponding baseline ones than WEWA, as well as a greater slope in both parity groups. Parous women presented greater amplitude and more pronounced downward trends for nonlinear parameters than nulliparous, especially for Sample and Spectral Entropy, which is associated with a greater predisposition to achieve APL that is corroborated by obstetric variables. Moreover, future efforts seem necessary to study in depth the differences between parity groups in order to correctly characterize and interpret their evolution.The collaboration of the HUP La Fe has been fundamental for the development of this work. In addition, the Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, EU RTI2018- 094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220) have supported it.Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Ye Lin, Y.; Garcia-Casado, J.; Nieto Del-Amor, F.; Diago-Almela, VJ.; Rey-Ferreira, I.... (2021). Comparative Study of Uterine Myoelectrical Response to Labour Induction Drugs in Nulliparous and Parous Women with Different EHG Analysis Techniques. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.9657548S1

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics