José Luis Blanco Murillo

Learn More
This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can(More)
We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients’ voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral(More)
Future Ubiquitous Sensor Networks (USNs) are expected to sense and combine multiple descriptions of user contexts, providing a huge potential for the development of revolutionary context-aware applications bridging the physical and digital worlds. Current Sensor Web initiatives aim to support ubiquitous access to sensor networks, but to make them really(More)
Whereas efficient and sensitive nanoheaters and nanothermometers are demanding tools in modern bio- and nanomedicine, joining both features in a single nanoparticle still remains a real challenge, despite the recent progress achieved, most of it within the last year. Here we demonstrate a successful realization of this challenge. The heating is magnetically(More)
This paper describes a novel architecture to introduce automatic annotation and processing of semantic sensor data within context-aware applications. Based on the well-known state-charts technologies, and represented using W3C SCXML language combined with Semantic Web technologies, our architecture is able to provide enriched higher-level semantic(More)
Current "Internet of Things" concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures,(More)
The aim of this paper is to study new possibilities of using Automatic Speaker Recognition techniques (ASR) for detection of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases can be very useful to give priority to their early treatment optimizing the expensive and timeconsuming tests of current diagnosis methods(More)
Past research on the speech of apnoea patients has revealed that resonance anomalies are among the most distinguishing traits for these speakers. This paper presents an approach to characterize these peculiarities using GMMs and distance measures between distributions. We report the findings obtained with two analytical procedures, working with a(More)