An algorithm that can help decode brain scans to identify the occurrence and type of epilepsy

To develop and train the algorithm, researchers examined EEG data from 88 human subjects. The team then analysed data and classified different wave patterns into sharp signals, spikes, and slow waves

Researchers at the Indian Institute of Science (IISc), in collaboration with AIIMS Rishikesh, have developed an algorithm that can help decode brain scans to identify the occurrence and type of epilepsy. 

Epilepsy is a neurological disease where the brain emits sudden bursts of electrical signals in a short amount of time, resulting in seizures, fits, and in extreme cases, death.

Based on the point of origin of the brain’s erratic signals, epilepsy is classified as either focal or generalised epilepsy. Focal epilepsy occurs when the erratic signals are confined to a specific region in the brain. If the signals are at random locations, then it is termed as generalised epilepsy.

In order to identify whether a patient is epileptic, neurophysiologists need to manually inspect EEGs (electroencephalograms), which can capture such erratic signals, explained an IISc press release.

Visual inspection of EEG can become tiring after prolonged periods, and may occasionally lead to errors, said Hardik J. Pandya, Assistant Professor at the Department of Electronic Systems Engineering (DESE) and the corresponding author of the study published in  Biomedical Signal Processing and Control.

The research aims to differentiate EEG of normal subjects from epileptic EEGs, and additionally, the developed algorithm attempts to identify the types of seizures. “Our work is to help the neurologists make an efficient and quick automated screening and diagnosis,” he added. 

To develop and train the algorithm, the researchers first examined EEG data from 88 human subjects acquired at AIIMS Rishikesh. Each subject underwent a 45-minute EEG test, divided into two parts: an initial 10-minute test when the subject was awake, which included photic stimulation and hyperventilation, followed by a 35-minute sleep period when the subject was asked to sleep.