We work in collaboration with both clinicians (cardiologists, neurologists, psychologists and psychiatrists) and companies in the health sector, involving all necessary stakeholders in designing solutions with impact in the field of eHealth.

  • Our “Telemedicine, mHealth, remote patient monitoring and personalized attention” line focuses on the interaction between health professionals and patients, or between health professionals through electronic means. We take advantage of the availability of electronic medical records, integrating them with current data obtained through different techniques -including mobile technologies- to improve the quality of diagnosis and adapt it to the needs and the particular profile of each patient.
  • Our “eHealth Data Analysis” line, involves the use of biostatistics, Data Mining, and Big Data technologies to improve diagnosis, healthcare, prognosis, risk stratification and clinical decision support. We develop tools based on Process Mining techniques: analyzing the logs generated by health public systems to extract knowledge of the whole process. Thus it is possible to discover paths that the patients have normally followed within the health system, identifying where “bottlenecks” happen, or check whether medical guidelines are followed or not. We work in the processing and analysis of biomedical signals so that clinicians can make decisions, or reduce the time of diagnosis, and fostering the new concept of personalized medicine. In Gradiant we are working on new ways to process those signals, including ECG, EEG, PPG and electrodermal activity; and on the integration of biosensors and other devices with mobile terminals, both using custom protocols or standards-based ones.


What does Gradiant bring to this sector?

  • Algorithms for biomedical signal processing, fall detection and detection of agitation in bedridden patients.
  • Contactless systems (based on UWB technology) for measurement of biomedical signals.
  • Bioinformatics: standard data analysis of massive sequencing data (NGS).
  • Development of mHealth systems, including the integration of sensor devices and the development of communication gateways.
  • Biometric access systems.
  • Anonymisation of sensitive data and privacy levels measurement.
  • Data processing in the encrypted domain.
  • Solutions for access control, encryption, integrity, key management, etc.
  • Secure systems design and support the definition of privacy and security requirements.
  • Capabilities in clinical decision supporting systems (CDSS), Big Data, pattern recognition and data mining.