Impact story summary

An epileptic seizure is a brief episode of symptoms due to abnormal excessive or synchronous neuronal activity in the brain caused by structural abnormalities, encephalitis, lack of oxygen, injury, tumour, and some dysfunctions of the brain.

More than 65 million individuals are diagnosed with epilepsy (i.e., spontaneous and recurrent seizures) worldwide. Approximately 325 million of the world population experience a seizure in their life time. Electroencephalogram (EEG) is a widely used tool to diagnose a possible epileptic seizure. Automated seizure (i.e., epileptic seizure) detection and prediction techniques have great potential in epilepsy monitoring, diagnosis, and rehabilitation. The detection and prediction accuracy of the existing techniques are not accurate enough due to the challenges in terms of non-abrupt phenomena and inconsistent signals in different brain locations with different patients, types (general/partial) of seizures, and hospital settings.

To overcome the limitations of existing methods, generic seizure detection approaches were developed through innovative feature extraction of ictal (actual period of seizure onset) and interictal (period between two adjacent seizures) EEG signals by identifying a specific range of frequencies, which can distinguish ictal and interictal signals using various established transformation and decomposition techniques.

The developed epilepsy seizure prediction method provides high prediction accuracy with low false alarms compared to the state-of-the-art methods considering a wide range of patients. The developed theoretical contributions provide an opportunity to develop a clinical device to predict forthcoming seizures in real time applications.
Impact date2014
Category of impactQuality of life Impact
Impact levelAdoption

Countries where impact occurred

  • Australia


  • epilepsy