Scalable solutions for early detection of neurological and psychiatric disorders


Čukić Radenković Milena Čukić Radenković

Milena Čukić Radenković

Empa Swiss Labs for Materials Science and Technology, Switzerland

Lipping Tarmo Lipping

Tarmo Lipping

Tampere University, Finland

Olejarzyk Elzbieta Olejarzyk

Elzbieta Olejarzyk

Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, AGH University of Science and Technology, Poland

Massaroni Carlo Massaroni

Carlo Massaroni

Università Campus Bio-Medico di Roma, Italy


During and after pandemic the realization came that our healthcare systems are not sustainable for our aging population, and waiting lists are sorely long in majority of European countries. After ini-tial visit the referral for many costly exams, like neuroimaging procedures and biochemical tests follows, that is simply not sustainable for majority of healthcare systems. Early detection and mon-itoring of patients' progress in cardiovascular diseases, psychiatric disorders and dementia risks (most importantly AD, as most frequent form up to 70% of all dementias) is hence the hot topic in Digital Health applications and it can minimize the Healthcare costs and improve the positive out-comes of therapy, as well as improve the quality of life of the patients greatly. Importantly such an early screening (or detection of certain biomarkers) could increase the effectivity of care, as costly diagnostic procedures are directed to those who already have detected some of known risks (and lead to minimization of false positives).

Here we want to invite fellow researchers to apply for early detection of biomarkers important for scalable solutions in particular for psychiatric disorders, dementia risks, movement disorders early detection as well as for cardiovascular disorders screening and monitoring solutions.

Also, we would like to receive original research results on explainable Ai applications in combina-tion with biomarkers detection in healthy aging population, as well as approaches that include pas-sive data collection, personalized online (or wireless) testing by patient herself or non-expert, ex-ergames cognitive variability testing, and the like.


Dr. Milena Čukić received her BSc (2000) in biomiedical electronics engineering and MSc (2001) at Scool of Electronics, University of Belgrade, Serbia. Consequently, she received Magisterium in General Physiology with Biophysics (2006) at School of Biology, and PhD in Neuroscience (2011) with FENS supported stay in Italy (2009) SISSA. Currently she is Senior Postdoc Researcher and Project Leader at Empa Swiss Federal Labs for Materials Science and Technology (Lab for Biomimetic Membarnes and Textiles) at St. Gallen, Switzeralnd, and Visiting Professor at BU, Serbia.
Her research interests are in interaction of human physiology with electromagnetic fields (NIBS), physiological complexity applications in various eraly detection projects (spanning from spinal cord injuries and BCI, to movement disorders and computational psychiatry), improving digital twins for transdermal fentanyl therapy for cancer patients (with special emphasis on anomalous diffusion, memory effect of human tissue), to innovations in digital health more broadly. She is also experienced lecturer in Physiological complexity and Cognitive Neuroscience and she is enthusiastic about Active inference approach. She mentored 7 PhD students.
She led several international projects consortia and WPs, and is active in NENS, FENS, EuADS, Claire, EACR, IFMBE, European Digital SME Alliance, as well as Founder of 3EGA B.V. the Netherlands.7 PhD students and is acting as an Review Editor in Frontiers Digital Health and Guest Editor in Frontiers in Psychiatry.

Prof. Tarmo Lipping got his MSc degree in 1993, Dr Tech degree in Signal Processing in 2001 and MBA degree in 2013, all from Tampere University of Technology (now Tampere University). In 2001-2002 he was postdoctoral research associate at Dartmouth College, NH, USA and in 2002-2003 he served as the Director of Biomedical Engineering Center and held professorship in Bio-medical Engineering in Tallinn University of Technology (now Taltech). Since 2004 he is profes-sor of Signal Processing in Tampere University. His research interests include biomedical signal analysis using machine learning techniques for brain monitoring in anesthesia and Intensive Care as well as for the detection of biomarkers of brain health in real-life situations. During his career, Tarmo Lipping has been Principal Investigator of numerous academic and industry-related re-search projects; he is author or co-author of over 100 research articles and has supervised over 60 Masters' and 7 Doctoral theses.

Assoc. Prof. Elzbieta Olejarczyk obtained her MSc in Physics from the University of Warsaw in 1990; M.Sc. in Faculty of Physics, University “La Sapienza” of Rome, 1992; Ph.D. in Technical and Engineering Sciences in 2003 and D.Sc. in 2022 from Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences, where she is working currently in the field of biomedical signal processing, analysis and modelling, in particular focused on estimation of functional brain connectivity analysis with the high-resolution EEG using linear and nonlinear dynamics methods. Moreover, she is an associate professor at AGH University of Science and Technology in Krakow. She developed two patented methods: (1) method of monitoring of the depth of anaesthesia based on Higuchi’s fractal dimension; and (2) method of automatic detection and analysis of the EEG sharp wave-slow wave patterns evoked by fluorinated inhalation anaes-thetics, in collaboration with Silesian University of Medicine in Poland and with Tampere Univer-sity of Technology and Tampere Hospital in Finland. She was involved in several international projects. Her programs were implemented in Tecnologias de Microelectrónica SA platform in frame of Advanced Sensor Development for Attention, Stress, Vigilance and Sleep/Wakefulness Monitoring (SENSATION project) in the Sixth Framework Programme EU. She was participated in six research internships in Italy, Finland and USA. Currently, she is a leader of two WPs in an international project on prediction and prevention of cardiovascular diseases in pre- and type 2 di-abetes in the frame of EU4Health programme. She is acting as Review Editor in two journals: Frontiers in Neuroscience and Frontiers in Human Neuroscience.

Dr. Carlo Massaroni received his BSc (2010) and MSc (2012) in Biomedical Engineering and Ph.D. in Bioengineering (2017) from Università Campus Bio-Medico di Roma (UCBM). Currently, he is Tenure-Track Assistant Professor at UCBM.
His research interests are focused on the design, development, and tests of sensors, measuring systems and devices for mechanical and thermal measurements, with particular emphasis on the design of wearable and unobtrusive systems for the measurement of physiological and behavioral parameters. He is principal investigator and WP leader in several ongoing national and international projects dealing with wearable and unobtrusive technologies for physiological monitoring and biomarkers estimations in the medical, occupational and sports fields.
He is a Senior Member of the IEEE, and serve as Secretary in IEEE Sensors Council Italy Chapter. He is also Associate Member of the "Wearable Biomedical Sensors & Systems" TC of the IEEE EMB.