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.

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.

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.

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.

People with autism spectrum disorder (ASD) manifest great heterogeneity in their atypical sensory behaviors. It is estimated that 95% of people with ASD have a Sensory Process Disorder (SPD). People with ASD feel the need to control what happens in their environment. However, it is inevitable that new situations occur during a person’s daily life. Therefore, it is important to monitor most of the circumstances they face in an attempt to predict the appearance of disorders that end up affecting their behavior. This paper presents the first steps towards the development of a system for knowing the value and effect on the SPD of different biological and environmental parameters. To obtain those variables, two electronic devices have been designed. The first one is an electronic system for capturing environmental variables such as luminosity or humidity, which is portable and mobile. The second electronic device is a soft wearable wristband which gets biological parameters. To know the effect of those variables on the SPD, a complete software platform has been implemented. Both devices upload day-to-day data to a cloud database where the information is stored in timeseries data of different parameters. The system uses the data to learn a personalized model that is designed to manage the SPD of the user. The main novelty is the use of sensor integration, data processing and machine learning techniques to develop a system able to classify the sensory load supported by a user with ASD while performing different activities. The results obtained so far prove the feasibility of the approach.