A versatile wearable based on reconfigurable hardware for biomedical measurements

In this work a versatile hardware platform based on reconfigurable devices is presented. This platform it intended for the acquisition of multiple biosignals, only requiring a reconfiguration to switch applications. This prototype has been combined with graphene-based, flexible electrodes to cover the application to different biosignals presented in this paper, which are electrocardiogram, electrooculogram and electromyogram. The features of this system provide to the user and to medical personnel a complete set of diagnosis tools, available both at home and hospitals, to be used as a triage tool and for remote patient monitoring. Additionally, an Android application has been developed for signal processing and data presentation to the user. The results obtained demonstrate the wide range of possibilities in portable/wearable applications of the combination of reconfigurable devices and flexible electronics, especially for the remote monitoring of patients using multiple biosignals of interest. The versatility of this device makes it a complete set of monitoring tools integrated in a reduced size device

Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

Automatic detection of premature ventricular contractions (PVCs) is essential for early identification of cardiovascular abnormalities and reduction of clinical workload. As the most prevalent arrhythmia, PVCs can cause cardiac failure or sudden death. The difficulty resides in extracting features that effectively reflect the electrocardiogram (ECG) signals. Transition networks (TN), which represent the transition relationships between various phases of a time series, are advantageous for capturing temporal dynamics. Therefore, in order to recognize PVCs, each heartbeat was firstly split into serval segments; then their statistical properties were calculated for the sequence construction; finally, network topology related features were extracted from TN constructed by these sequences of statistical properties, and input into decision trees-based Gentleboost for PVC recognition. The algorithm was trained on MIT-BIH arrhythmia database (MIT-BIH-AR), and tested on St. Petersburg Institute of Cardiological Technics 12-lead arrhythmia database (INCART), wearable ECG database (WECG), and noise stress test database by four evaluation metrics: sensitivity, positive predictivity, F1-score (F1) and area under the curve (AUC). The proposed algorithm achieved an average F1 of 0.9784 and AUC of 0.9975 on MIT-BIH-AR, and proved good generalization ability on INCART and WECG with F1=0.9633 and 0.9467, AUC=0.9887 and 0.9755, respectively. The algorithm also exhibited robustness and noise immunity as evidenced by tests on sensitivity of R-wave peak offset and noise, and real-world daily life conditions. Overall, the proposed PVC detection algorithm based on TN theory offered high classification accuracy, strong robustness, and good generalization ability, with great potential for wearable mobile applications

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Evaluación de técnicas para la estimación del ritmo cardíaco

Según datos de la Organización Mundial de la Salud, las enfermedades cardiovasculares son la principal causa de defunción en el mundo. En 2012 se registraron 17.5 millones de muertes por esta causa, lo cual representa un 31% del total de muertes mundiales. De este número, más de tres cuartas partes se produjeron en países de ingresos medios y bajos [1]. Si nos restringimos al ámbito nacional, en 2014 se registraron 128.169 muertes por este tipo de enfermedades, suponiendo un coste para las arcas españolas de 7.700 millones de euros [2]. Se prevé que el gasto dedicado a tratar las anomalías cardíacas siga creciendo hasta alcanzar un coste per cápita de 180 euros en 2020, frente a los 124 de 2014. Por estos motivos, muchos investigadores dedicados al campo de la medicina han puesto sus ojos en la investigación sobre enfermedades cardiovasculares, intentando mejorar las formas tradicionales de diagnóstico existentes en los centros hospitalarios. En la actualidad, los diagnósticos por imagen [3] son una de las herramientas más utilizadas para la prevención de las enfermedades cardiovasculares. Otra prueba muy común es el electrocardiograma (ECG), ya que es una prueba no invasiva, simple, barata y capaz de ofrecer mucha información al facultativo para un correcto diagnóstico...Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació