A team of IT researchers working in project SmartHeart aims to leverage and develop novel deep learning architectures that can solve fundamental inverse problems in heart sound analysis. The research team wants to explore promising research avenues in the application of powerful computational tools that are expected to boost the performance and robustness of current signal processing approaches in the field of automatic cardiac auscultation with relevant impact on society.
Heart sounds are difficult to identify and analyze by the human listener, with some studies showing that only around 20% of medical interns can effectively perform cardiac auscultation. This motivates exploring the powerful combination of electronic stethoscopes and portable computer technology to create usable point-of-care Computer-Assisted Decision (CAD) systems for auscultation. The practical implementation of CAD systems for auscultation encounter some key challenges related to the processing and analysis of the heart sound signal, i.e., the phonocardiogram (PCG), due to various reasons: presence of different kinds of noise due to uncontrolled auscultation conditions in real environments, non-stationarity of the signal features, inter and intra-patient variability of the PCG characteristics, etc. In fact, a relevant part of the information contained in the PCG can only be unlocked if different components of the heart sound signal are reliably detected and separated.
The inverse problem consisting in the separation of components from a heart sound recorded with a single microphone turns out extremely challenging, due to the large time-frequency overlap of some components, as well as their similar morphological signatures. In this sense, more sophisticated processing algorithms for the solutions of inverse problems for heart sound analysis are in order. Recent results have shown that deep learning architectures can represent a valuable tool in solving inverse problems.
The most crucial hardware requirement needed to perform the simulations and experiments is represented by a powerful GPU. Recently, the project has received an important backup from the NVIDIA Corporation, which donated an NVIDIA GeForce Titan Xp. Mounted on a desktop workstation, the Titan Xp GPU represents an ideal setup for the SmartHeart team experiments.
SmartHeart joins three of IT research groups, which have independently created technology and accumulated knowledge in the field of cardiac sensing and signal processing. This project builds upon original work partially developed by the team in the field of cardiac sensing and signal processing within projects such as “DigiScope”, “HeartSafe”, “ICT for Future Health”, “HeartBIT” and “BITalino” (http://bitalino.com).
IT´s integration of ORCID was awarded the Authenticate, Collect, Display, and Connect badges of ORCID´s Collect and Connect program - a set of resources and guidelines for ORCID integration and engagement – thus becoming the first organization in Portugal to achieve this integration level.
Gathering a global community of all kinds of stakeholders from the research ecosystem, ORCID is a non-profit organization that aims to help all who participate in research, scholarship and innovation to have a unique identification and to be connected to their contributions and affiliations, across disciplines, borders and time.
With hundreds of ORCID Member Organizations globally building ORCID identifiers into their systems, as well as some funders and publishers now requiring ORCID iDs, researchers are increasingly likely to encounter ORCID in their day-to-day life. Therefore, the ORCID Collect & Connect program has been developed to streamline the integration process and foster a shared user experience, ensuring researchers and member-integrators understand what ORCID is, and why and how to engage.
It is essential that organizations play their part in planning and building an ORCID integration system that enables connecting validated, trustworthy information about its researchers and their affiliation with the organization to their ORCID records. ORCID evaluates the level of integration in each system according to five parameters: Authenticate and Collect – means that the system collects validated ORCID ID´s for individuals, ensuring they are correctly connected with the institution; Display – ID´s are displayed on the organization website, platform, systems, meaning they are plumbed to support ORCID ID´s; Connect – data is connected to ORCID Records, enabling researchers to provide validated data to others; and Synchronize – the organization system is synchronized with the ORCID Registry, which improves reporting speed and accuracy, reducing the burden of entering the data manually