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.

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.

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.