Data mining in neurological medicine
Pattern recognition algorithms
At a glance
- Project leader : Dr. Simone Ulzega
- Project team : Dr. Anna Castelnovo, Prof. Dr. Andrea Danani, Dr. Massimo Maiolo, Prof. Dr. Mauro Manconi
- Project budget : CHF 28'000
- Project status : completed
- Funding partner : Internal
- Project partner : Scuola universitaria professionale della Svizzera italiana SUPSI / Dipartimento tecnologie innovative, Ente Ospedaliero Cantonale EOC / Neurocentro
- Contact person : Simone Ulzega
Description
Restless legs syndrome (RLS, Willis-Ekbom disease) is a
neurological movement disorder characterised by motor and sensory
symptoms, such as the uncontrollable need to move the legs (and
sometimes also the arms). Such need is associated with an
unpleasant and disturbing sensation in the lower limbs that
typically worsens during rest and that can be partially or totally
relieved by voluntary or involuntary movement. Thus, sleep
disturbance becomes of paramount significance to RLS patients.
Indeed, severe RLS produces the most extreme chronic sleep loss
among all known sleep disorders, with obvious dramatic consequences
for normal day-time functions of the human body. Moreover, RLS is
frequently associated with other sleep disorders (e.g., PLMS,
insomnia) resulting in daytime fatigue and a significant
deterioration of the quality of life. Although no large-scale
studies are available, it is estimated that 7 to 15% of the adult
population presents symptoms compatible with RLS, and 20-40% of
these patients report significant impairment of life quality.
Typically, the prevalence of RLS increases with age, but each
segment of the population (including children and the elderly) may
present symptoms and genetically transmitted forms of the syndrome
tend to have greater clinical severity and early onset. In spite of
the dramatic consequences that the RLS can have on people’s life
quality, this syndrome is often underestimated and
under-diagnosed.
In the past, there has already been a collaboration between Dr.
Massimo Maiolo (ex-SUPSI, now ZHAW, IAS), Prof. Danani (ex-SUPSI,
now IDSIA Institute for Artificial Intelligence Research) and Dr.
med. Manconi (Neurocenter of the Civic Hospital of Lugano,
Inselspital Bern, Lugano Master School of Medicine). The
collaboration led to the development of a prototype program for the
calculation of synchronisation parameters between the two brain
hemispheres on a patient affected by hydrocephalus. We are planning
now a new collaboration with all the partners mentioned above
aiming at developing suitable data mining algorithms to investigate
not-yet-understood RLS features. In particular, the goal is to
better describe the interaction/synchronization between the 3
different systems involved in RLS, that is, nervous central-,
cardiovascular- and motor systems. The method and data processing
protocol developed in the first phase of the collaboration by Dr.
Massimo Maiolo will play a central role, since we are planning to
adapt parts of them to suit the new application (RLS) in a new
high-performance computing framework. The kick-off funding would
cover a preliminary implementation and performance evaluation, on
real medical/neurological data, of the algorithms that will be
applied to RLS polysomnography time-series (i.e., sleep-time
time-series of brain waves, blood oxygen levels, heart and
breathing rates, eye and legs movements). These time-series are
very large and highly multi-dimensional data sets, which make data
mining computationally extremely expensive. It is therefore
essential to adequately integrate the new algorithms in a HPC
framework, which would be conveniently provided by our HPC facility
in Wädenswil.
The long-term goal of the collaboration is to obtain funding in the
context of a SNF project. The partners mentioned above, Prof.
Danani and Dr. med. Manconi, renowned and well-known experts in
their fields, are confident that our chances to obtain such funding
are high. Therefore, as a part of this kick-off project we would
draft a project proposal that will then be submitted to the SNF in
2020.
The methods that we are planning to explore and apply are very
general and suitable for a broad range of potential applications in
a variety of physical and life sciences. The project will give us
the opportunity to significantly enhance our experience with signal
analysis, data mining and machine learning. Many activities at the
Institute of Applied Simulations could greatly benefit from this
enriched expertise. Time-series analysis and data mining methods
are hot topics in the broad research field of Big Data. Moreover,
the methods that we are planning to apply rely very heavily on
cluster and parallel computing (HPC). This project will therefore
be a very exciting opportunity to exploit the power of the newly
established HPC facility of the ZHAW in Wädenswil and strengthen
our know-how in high-performance computing, code parallelisation
and software optimisation for cluster applications.
Publications
-
Castelnovo, Anna; Amacker, Julian; Maiolo, Massimo; Amato, Ninfa; Pereno, Matteo; Riccardi, Silvia; Danani, Andrea; Ulzega, Simone; Manconi, Mauro,
2022.
High-density EEG power topography and connectivity during confusional arousal.
Cortex.
(155), pp. 62-74.
Available from: https://doi.org/10.1016/j.cortex.2022.05.021