Epilepsy is a chronic neurological disorder which is characterized by recurrent and sudden seizures. Many people with epilepsy have more than one type of seizure and may have other symptoms of neurological problems as well. Electroencephalography is an important clinical tool for diagnosing, monitoring and managing neurological disorders related to epilepsy. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. In today’s world where adult life expectancy is rising and humans are living longer than ever before, the healthcare system generates vast amounts of data, including EEG signals. We examine the ways in which we can efficiently utilize this data in order to optimize seizure detection to improve the patients’ quality of life. We also explore how Machine Learning can be applied to extract features and analyze the EEG signals and propose methods to achieve high classification accuracy.
Introduction (300)
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If left undetected, epileptic seizures can severely impact the lives of patients, causing fatal accidents and unexpected death. While the condition can improve with medication, for about 30% of patients, they are non-curative and patients can have seizures even after surgery [1].
Around 50 million people worldwide suffer from Epilepsy and the condition can affect a person at any age. While it is possible to visually inspect an EEG report and interpret it, a level of subjectivity is introduced to the process of seizure detection and it tends to be time-consuming and tedious as the EEG data lasts for several hours. Thus it would be preferable to automate the detection process and reduce the human effort required for