14 research outputs found

    Non-Invasive Device for Blood Pressure Wave Acquisition by Means of Mechanical Transducer

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    Blood pressure wave monitoring provides interesting information about the patient’s cardiovascular function. For this reason, this article proposes a non-invasive device capable of capturing the vibrations (pressure waves) produced by the carotid artery by means of a pressure sensor encapsulated in a closed dome filled with air. When the device is placed onto the outer skin of the carotid area, the vibrations of the artery will exert a deformation in the dome, which, in turn, will lead to a pressure increase in its inner air. Then, the sensor inside the dome captures this pressure increase. By combining the blood pressure wave obtained with this device together with the ECG signal, it is possible to help the screening of the cardiovascular system, obtaining parameters such as heart rate variability (HRV) and pulse transit time (PTT). The results show how the pressure wave has been successfully obtained in the carotid artery area, discerning the characteristic points of this signal. The features of this device compare well with previous works by other authors. The main advantages of the proposed device are the reduced size, the cuffless condition, and the potential to be a continuous ambulatory device. These features could be exploited in ambulatory tests.D.Z.V. thanks the economic support of the ACIF (subsidies for the recruitment of predoctoral research staff) program with the scholarship ACIF/2019/058. The work of J.M.V.-S. was supported by Conselleria d’Educació, Investigació, Cultura i Esport (GVA) through FDGENT/2018/015 project. The work of C.G.J. was funded by the Spanish Ministry of Education, Culture, and Sport through the Research and Doctorate Supporting Program FPU, grant number FPU14/00401. This work was partially funded by Spanish Research State Agency and European Regional Development Fund through “Craneeal” Project (DPI2106-80391-C3-2-R)

    Sistema no invasivo para la medida y visualizaciĂłn de desplazamientos de tejidos en neurocirugĂ­a

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    [ES] Se presenta un sistema de imagen mĂ©dica basado en mediciĂłn de distancias entre tejidos por medio de microondas. El sistema estĂĄ pensado para la monitorizaciĂłn y correcciĂłn del problema de brain-shift en operaciones de tumores cerebrales. Se muestra el sistema desarrollado y los primeros resultados con phantoms de tejidos cerebrales que tienen una morfologĂ­a similar a los tejidos reales. Se demuestra la viabilidad del sistema para la medida de distancias y la reconstrucciĂłn de una imagen 3D intraoperatoria.[EN] A medical imaging system based on measuring distances between tissues by means of microwaves is presented. The system is designed for the monitoring and correction of the brain-shift problem in brain tumor operations. It shows the developed system and the first results with phantoms of brain tissues that have a morphology similar to real tissues. The viability of the system for the measurement of distances and the reconstruction of an intraoperative 3D image is demonstrated.Juan, C.; Blanco, C.; Herrero, N.; Garcia, H.; Vicente-Samper, J.; Avila, E.; Sabater-Navarro, J. (2019). Sistema no invasivo para la medida y visualizaciĂłn de desplazamientos de tejidos en neurocirugĂ­a. En 11Âș Simposio CEA de BioingenierĂ­a. Editorial Universitat PolitĂšcnica de ValĂšncia. 76-84. https://doi.org/10.4995/CEABioIng.2019.10033OCS768

    Intelligent Monitoring Platform to Evaluate the Overall State of People with Neurological Disorders

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    The percentage of people around the world who are living with some kind of disability or disorder has increased in recent years and continues to rise due to the aging of the population and the increase in chronic health disorders. People with disabilities find problems in performing some of the activities of daily life, such as working, attending school, or participating in social and recreational events. Neurological disorders such as epilepsy, learning disabilities, autism spectrum disorder, or Alzheimer’s, are among the main diseases that affect a large number of this population. However, thanks to the assistive technologies (AT), these people can improve their performance in some of the obstacles presented by their disorders. This paper presents a new system that aims to help people with neurological disorders providing useful information about their pathologies. This novelty system consists of a platform where the physiological and environmental data acquisition, the feature engineering, and the machine learning algorithms are combined to generate customs predictive models that help the user. Finally, to demonstrate the use of the system and the working methodology employed in the platform, a simple example case is presented. This example case carries out an experimentation that presents a user without neurological problems that shows the versatility of the platform and validates that it is possible to get useful information that can feed an intelligent algorithm.This work was partially funded by Spanish Research State Agency and European Regional Development Fund through “Race” Project (PID2019-111023RB-C32). The work of J.M.V.-S. is supported by the Conselleria d’Educació, Investigació, Cultura i Esport (GVA) through FDGENT/2018/015 project

    Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings

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    Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages

    Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings

    No full text
    Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages

    Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

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    Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients’ health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people

    Low-Cost Direct-Writing of Silver-Based Ink for Planar Microwave Circuits up to 10 GHz

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    Direct ink writing (DIW) of conductive ink is a printed electronics technology that allows a variety of electronic circuits to be produced in a simple way and with minimal waste of materials. In recent years it has been used for rapid prototyping of RF circuits typically working at S-band frequencies (2–4 GHz). In an attempt to extend this frequency range while maintaining cost-effective prototyping, this work has focused on proving the feasibility of DIW of silver-conductive (SC) ink for the fabrication of planar microwave circuits beyond 10 GHz, more specifically, ultra-wideband (UWB) antennas for medical applications. For this purpose, the DC and RF performance of the SC ink, as well as the FR4 substrate used, were first evaluated. Based on the comparison between experimental and simulated results, we have found that the effective RF conductivity of the SC ink is approximately 27.6% of its DC value and 3.4% of the copper conductivity. A few test microstrip circuits were fabricated by DIW, namely two S-band filters and one UWB antenna. The overall measured performance of all of them agreed well with simulations. In particular, the DIW antenna exhibited a bandwidth of 8.2 GHz (between 2.4 and 10.6 GHz), and was compared with an identical copper antenna showing that both have very similar characteristics. It was also found that the lower conductivity of SC ink as compared to copper led to a gain reduction of only 0.3 dB
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