The current diagnostic process is quite lengthy and may involve multiple EEG recordings to detect abnormal activities.
The Monash study has monitored over 400 EEG recordings of patients with and without epilepsy from Alfred Health and The Royal Melbourne hospital.
This dataset has been used to demonstrate how a particular machine learning model can help recognise the signs of epilepsy and facilitate the automated labelling of EEG data.
Epilepsy research fellow and PhD candidate Mubeen Janmohamed was responsible for identifying suitable EEGs and labelling them manually, while fellow researcher and PhD candidate Duong Nhu was responsible for ensuring the machine learning model was able to interpret and learn from two different datasets.
"The collaboration between Alfred Health and The Royal Melbourne hospital allowed our team to collect two large datasets of routine EEG recordings, which we then used to model our machine learning algorithm," Nhu said.
"Being able to apply a machine learning model across various datasets demonstrates our ability to create an algorithm that is more reliable, adaptive and intelligent than existing models, making our model more useful when applied in real-world scenarios such as diagnosing patients in a clinic."
Professor Patrick Kwan of the Department of Neuroscience said "Our plans for this research will be to continue to improve the current models and further train it against additional datasets from other hospitals. We aim to develop an accurate algorithm which will be reliable across multiple hospital settings and usable in the early stages of epilepsy diagnosis, from both routine and sleep-deprived EEG recordings."
"An algorithm like this would also be of value in low GDP countries or remote regions where there is limited expertise in interpreting EEG scans, a task which generally requires many years of training," he added.
Another possible application is to use as a training tool for graduate neurologists.
This project was carried out as part of the Graduate Research Industry Partnerships (GRIP) program, in which Monash University PhD students work with academic leaders from various fields with external industry partners to address issues of global significance.