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Researchers at the Indian Institute of Science (IISc), Bengaluru, have teamed up with the All India Institute of Medical Sciences (AIIMS) Rishikesh to develop an algorithm to decode brain scans for the diagnosis of epilepsy, its type and duration of occurrence. A patent has been filed for it and the algorithm is being tested for its reliability by physicians at AIIMS Rishikesh.
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, fatality. 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, said IISC researchers.
Currently to diagnose if a patient is epileptic, neurophysiologists need should manually inspect EEGs (electroencephalograms), which can capture such erratic signals. Visual inspection of EEG can become tiring after prolonged periods, and may occasionally lead to errors, pointed out Hardik J Pandya, Assistant Professor, 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. 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,” stated Pandya.
In their study, the team reports a novel algorithm that can sift through EEG data and identify signatures of epilepsy from the electrical signal patterns. After initial training, the algorithm was able to detect whether a human subject could have epilepsy or not based on these patterns in their respective analyses with a high degree of accuracy, said IISc.
During the researchers, first the EEG data was examined from 88 human subjects acquired at AIIMS Rishikesh. Each subject underwent a 45-minute EEG test, divided into two parts. First was 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. Second the team analysed this data and classified different wave patterns into sharp signals, spikes, and slow waves.
An epileptic subject shows a different set of patterns compared to a healthy individual. The researchers developed an algorithm to calculate the total number of sharp waves. The Cumulative Sharp Count parameter is used to detect if the subject is epileptic or not. The algorithm also calculates the sum of areas under the spikes and sharp curves to distinguish between focal and generalised epilepsy. The researchers also noted that the study indicated a way to identify absence seizures using a Cumulative Spike-Wave Count. This is because in some cases, these absence seizures are critical and can be fatal.
The team then ran their algorithm on a new set of EEG data from subjects for whom the classification on both epileptic and non-epileptic cases. The blind validation study successfully classified the subjects accurately in nearly 91% of the cases.
“We hope to refine this further by testing on more data to consider more variabilities of human EEGs until we reach the point where this becomes completely translational and robust,” says Rathin K Joshi, PhD student, DESE and first author of the study.
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