Staff member

Yolanda Castillo Escario

PhD Student
Biomedical Signal Processing and Interpretation
+34 934 020 559
Staff member publications

Castillo, Y., Blanco, D., Whitney, J., Mersky, B., Jané, R., (2017). Characterization of a tooth microphone coupled to an oral appliance device: A new system for monitoring OSA patients Engineering in Medicine and Biology Society (EMBC) 39th Annual International Conference of the IEEE , IEEE (Seogwipo, South Korea) , 1543-1546

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese populations. Despite constituting a serious health, social and economic problem, most patients remain undiagnosed and untreated due to limitations in current equipment. In this work, we propose a novel method to diagnose OSA and monitor therapy adherence and effectiveness at home in a non-invasive and inexpensive way: combining acoustic analysis of breathing and snoring sounds with oral appliance therapy (OA). Audiodontics has introduced a new sensor, a tooth microphone coupled to an OA device, which is the main pillar of this system. The objective of this work is to characterize the response of this sensor, comparing it with a commercial tracheal microphone (Biopac transducer). Signals containing OSA-related sounds were acquired simultaneously with the two microphones for that purpose. They were processed and analyzed in time, frequency and time-frequency domains, in a custom MATLAB interface. We carried out a single-event approach focused on breaths, snores and apnea episodes. We found that the quality of the signals obtained by both microphones was quite similar, although the tooth microphone spectrum concentrated more energy at the high-frequency band. This opens a new field of study about high-frequency components of snores and breathing sounds. These characteristics, together with its intraoral position, wireless option and combination with customizable OAs, give the tooth microphone a great potential to reduce the impact of sleep disorders, by enabling prompt detection and continuous monitoring of patients at home.

Keywords: Microphones, Teeth, Sleep apnea, Time-frequency analysis, Signal to noise ratio, Monitoring, Acoustics

Castillo, Y., Camara, M. A., Blanco-Almazan, D., Jane, R., (2017). Characterization of microphones for snoring and breathing events analysis in mHealth Engineering in Medicine and Biology Society (EMBC) 39th Annual International Conference of the IEEE , IEEE (Seogwipo, South Korea) , 1547-1550

Obstructive sleep apnea (OSA) is one of the most common sleep disorders, especially in elderly population. Despite its high prevalence and severe consequences, most patients remain undiagnosed due to serious limitations on the existing equipment. Efforts are being done to find cost-effective alternatives and mHealth solutions could play a key role. One promising approach in this context is the acoustic analysis of snoring. The sensor it requires is a microphone, which is widely available in different models and even integrated in smartphones. The objective of this work is to characterize and compare the responses of two commercial tracheal microphones and a mHealth-based microphone, as a proof-of-concept to evaluate their potential as sensors for OSA detection. To do that, we designed an experimental protocol to study OSA-related events (breaths, snores and apneas) simulated by 4 subjects. Test signals were simultaneously recorded with different microphones and posteriorly processed and analyzed. We accurately characterized the frequency response of the two commercial microphones, finding that one of them was too restrictive (bandwidth 50-250 Hz) and thus not suitable as snoring sensor for high-frequency acoustic analysis. Regarding smartphones, we studied the Samsung Galaxy S5 microphone. We found that, when located over the thorax, it provided quality signals comparable to those of tracheal microphones, with a broader frequency response. Further work is required, but this preliminary study suggests that acoustic analysis of snoring through mHealth solutions can be a feasible alternative to screen and monitor OSA patients at home.

Camara, M. A., Castillo, Y., Blanco-Almazan, D., Estrada, L., Jane, R., (2017). MHealth tools for monitoring Obstructive Sleep Apnea patients at home: Proof-of-concept Engineering in Medicine and Biology Society (EMBC) 39th Annual International Conference of the IEEE , IEEE (Seogwipo, South Korea) , 1555-1558

Obstructive Sleep Apnea (OSA) is a sleep disorder that affects mainly the adult and elderly population. Due to the high percentage of patients who remain undiagnosed and untreated because of limitations of current diagnosis methods, the management of OSA is an important social, scientific and economic problem that will be difficult to be assumed by health systems. On the other hand, smartphone platforms (mHealth systems) are being considered as an innovative solution, thanks to the integration of the essential sensors to obtain clinically relevant parameters in the same device or in combination with wireless wearable devices.

Keywords: Sleep apnea, Microphones, Monitoring, Sensors, Accelerometers, Biomedical monitoring, Band-pass filters

Castillo, Y., Blanco, D., Cámara, M.A., Jané, R., (2016). Study of time-frequency characteristics of single snores: extracting new information for sleep apnea diagnosis CASEIB Proceedings XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2016) , Sociedad Española de Ingeniería Biomédica (Valencia, Spain) , 105-108

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese population. Despite constituting a huge health and economic problem, most patients remain undiagnosed due to limitations in current strategies. Therefore, it is essential to find cost-effective diagnostic alternatives. One of these novel approaches is the analysis of acoustic snoring signals. Snoring is an early symptom of OSA which carries pathophysiological information of high diagnostic value. For this reason, the main objective of this work is to study the characteristics of single snores of different types, from healthy and OSA subjects. To do that, we analyzed snoring signals from previous databases and developed an experimental protocol to record simulated OSA-related sounds and characterize the response of two commercial tracheal microphones. Automatic programs for filtering, downsampling, event detection and time-frequency analysis were built in MATLAB. We found that time-frequency maps and spectral parameters (central, mean and peak frequency and energy in the 100-500 Hz band) allow distinguishing regular snores of healthy subjects from non-regular snores and snores of OSA subjects. Regarding the two commercial microphones, we found that one of them was a suitable snoring sensor, while the other had a too restricted frequency response. Future work shall include a higher number of episodes and subjects, but our study has contributed to show how important the differences between regular and non-regular snores can be for OSA diagnosis, and how much clinically relevant information can be extracted from time-frequency maps and spectral parameters of single snores.

Castillo, Y., Gutiérrez, A., (2015). Determining the tertiary structure of an olfactory receptor CASEIB Proceedings XXXIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2015) , Sociedad Española de Ingeniería Biomédica (Madrid, Spain) , 245-248

Olfactory receptors (ORs) are transmembrane proteins that interact with odorant molecules, triggering the first step in the mechanism of olfaction. They also occupy a large part of mammalian genome and their dysfunction is involved in some degenerative diseases. Proteins structure is related to their function, but experimentally determining the three-dimensional structure of ORs is tricky. However, recent advances in bioinformatics have made it easier. The objectives of this project were to computationally model several ORs and to study their interactions with odorants. We carried out a comparative modelling process, using Chimera and Modeller programs as alignment, visualization and 3D reconstruction tools. After validating the models by several procedures, we performed docking experiments with DOCK. We predicted binding pockets location and studied which odorants bind which ORs, comparing our results with electrophysiological measures of olfactory neurons activity. At the end, we successfully built six mouse ORs models and tested them against 24 common odorants, obtaining a good correlation with previous studies and thus validating our protocol. Moreover, we reconstructed eight human ORs which had never been modelled before, though their predicted interactions with 63 odorants were not clearly correlated to previous experimental studies. In this way, we have shown the efficiency of these computational algorithms, which can contribute to the research about biological processes such as the mechanism of olfaction. With small improvements, they could be in an early future a suitable alternative to experimental approaches, leading to accurate protein models in a practical, faster and easier way.