An epileptic seizure, which is caused by disruptions in the normal electrical activity of the brain, can produce a range of symptoms including convulsions and unconsciousness. To learn more about the timing and nature of seizures, the electrical activity of patients’ brains is often recorded using electroencephalograms (EEGs).
At the moment, however, epilepsy experts must review these recordings manually, which is a time-consuming process.
’EEG recordings may cover a period of several weeks,’ said Rajeev Agarwal, a professor in Concordia’s Department of Electrical and Computer Engineering. ’That’s a lot of data to review. Automating the process is difficult, because there’s no exact definition for a seizure, so there’s no template to look for. Every seizure is different with every patient.’
However, seizures have certain recognisable characteristics. They occur when neurons fire in a synchronous or rhythmic manner. As seizures progress, the EEG signals have very strong transitions. Seen on an EEG recording, the waves of electrical activity tend to be spike-like.
The Concordia team, led by PhD candidate Rajeev Yadav, devised an algorithm to check the sharpness of the electrical signals on the EEG recordings.
The approach proved extremely successful. In the study of EEG recordings of seven patients, the new method detected every seizure while scoring a low rate of false positives. The researchers claim that results are far better than those obtained with existing methods.
The research team is continuing to evaluate and refine this method of seizure detection. So far, according to Agarwal, who is also co-founder, chief technical officer and vice-president of Leap Medical, results are promising.