UCC discovery hailed as ‘game changer’
Michael Olney
A team of researchers at UCC has discovered a revolutionary AI-assisted method for analysing the brainwaves of new-born babies.
The ingenious new approach uses sound, rather than visual images to analyse the brainwaves and has the potential to save many vulnerable babies’ lives.
The method is more accessible to a wider cohort of medical professionals and brings both accuracy and speed while requiring no training to be adopted in clinical settings.
The AI-driven mechanism works by converting brainwaves to sound. Human ears are more sensitive to changes in frequency and evolution of morphology in time, which is the signature of many seizures. By focusing the listener’s attention to interesting segments in the recording, EEG seizures can be distinctly heard.
The interdisciplinary team from UCC’s findings have been published in the international journal Nature Scientific Reports.
Analysing brainwaves or Electroencephalograms (EEG) is considered the gold standard in detecting anomalies in brain activity such as seizures. The new method has the potential to make EEG monitoring more prevalent in medical settings, including those in disadvantaged communities.
It also reduces the burden of analysing EEG data, allowing 2 hours of EEG to be screened in just three seconds.
The method extends the concept of the stethoscope used by doctors to listen to heart, lung or other sounds from a patient’s body.
While neurophysiologists use EEG recordings to identify seizures visually, it is a slow and cumbersome process for the medic, which involves scrolling through thousands of images. The expertise also requires a significant amount of training that is not readily available on a continuous basis in all hospitals.
Prof. Andriy Temko, co-supervisor of the study, School of Engineering and Architecture, UCC, said a lack of interpretation expertise has always been a bottleneck in the widespread usage of EEG monitoring in new-borns: “To address that, previous research has focused on developing black box AI models to analyse EEG signals. While outstanding performances were obtained, the practical applicability was limited. We have developed a method where AI augments human senses in an explainable manner to keep a healthcare professional in the decision-making loop. It is a potential game changer in the EEG monitoring industry.”
Dr Emanuel Popovici, co-supervisor of the study, School of Engineering and Architecture, UCC, added: “This potentially high-impact research further demonstrates the importance of interdisciplinary research and the power of openly available EEG data. It is another great example of the type of projects which can better humanity.”