Researchers from Zurich have developed a compact, energy-efficient device
made from artificial neurons that is capable of decoding brainwaves. The
chip uses data recorded from the brainwaves of epilepsy patients to identify
which regions of the brain cause epileptic seizures. This opens up new
perspectives for treatment.
Current neural network algorithms produce impressive results that help solve
an incredible number of problems. However, the electronic devices used to
run these algorithms still require too much processing power. These
artificial intelligence (AI) systems simply cannot compete with an actual
brain when it comes to processing sensory information or interactions with
the environment in real time.
Neuromorphic chip detects high-frequency oscillations
Neuromorphic engineering is a promising new approach that bridges the gap
between artificial and natural intelligence. An interdisciplinary research
team at the University of Zurich, the ETH Zurich, and the UniversityHospital
Zurich has used this approach to develop a chip based on neuromorphic
technology that reliably and accurately recognizes complex biosignals. The
scientists were able to use this technology to successfully detect
previously recorded high-frequency oscillations (HFOs). These specific
waves, measured using an intracranial electroencephalogram (iEEG), have
proven to be promising biomarkers for identifying the brain tissue that
causes epileptic seizures.
Complex, compact and energy efficient
The researchers first designed an algorithm that detects HFOs by simulating
the brain's natural neural network: a tiny so-called spiking neural network
(SNN). The second step involved imple-menting the SNN in a fingernail-sized
piece of hardware that receives neural signals by means of electrodes and
which, unlike conventional computers, is massively energy efficient. This
makes calculations with a very high temporal resolution possible, without
relying on the internet or cloud computing. "Our design allows us to
recognize spatiotemporal patterns in biological signals in real time," says
Giacomo Indiveri, professor at the Institute for Neuroinformatics of UZH and
ETH Zur-ich.
Measuring HFOs in operating theaters and outside of hospitals
The researchers are now planning to use their findings to create an
electronic system that reliably recognizes and monitors HFOs in real time.
When used as an additional diagnostic tool in operating theaters, the system
could improve the outcome of neurosurgical interventions.
However, this is not the only field where HFO recognition can play an
important role. The team's long-term target is to develop a device for
monitoring epilepsy that could be used outside of the hospital and that
would make it possible to analyze signals from a large number of electrodes
over several weeks or months. "We want to integrate low-energy, wireless
data communications in the design - to connect it to a cellphone, for
example," says Indiveri. Johannes Sarnthein, a neurophysiologist at
UniversityHospital Zurich, elaborates: "A portable or implantable chip such
as this could identify periods with a higher or lower rate of incidence of
seizures, which would enable us to deliver personalized medicine."
Reference:
Sharifshazileh M, Burelo K, Sarnthein J, Indiveri G. An electronic
neuromorphic system for real-time detection of high frequency oscillations
(HFO) in intracranial EEG. Nature Communications. 2021;12(1):3095.
doi:10.1038/s41467-021-23342-2
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Medical Science