KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy from EEG Signals. A Detailed Analysis

Epilepsy is a chronic disorder, the hallmark of which is recurrent, unprovoked seizures. Many people with epilepsy have more than one type of seizures and may have other symptoms of neurological problems as well. Epilepsy is caused due to sudden recurrent firing of the neurons in the brain. The symptoms are convulsions, dizziness and confusion. One out of every hundred persons experiences a seizure at some time in their lives. It may be confused with other events like strokes or migraines. Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its process still is hardly understood.
In India, the number of persons suffering from epilepsy is increasing every year. The complexity involved in the diagnosis and therapy has to be cost effective in. In this project, the authors applied an algorithm which is used for a classification of the risk level of epilepsy in epileptic patients from Electroencephalogram (EEG) signals. Dimensionality reduction is done on the EEG dataset by applying Power Spectral density. The KNN Classifier and K-Means clustering is implemented on these spectral values to epilepsy risk level detection. The Performance Index (PI) and Quality Value (QV) are calculated for the above methods. A group of twenty patients with known epilepsy findings are used in this study.

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Chapter 1.4 EEG SIGNALS FOR EPILEPSY DETECTION:
Epileptic seizures result from a temporary electrical disturbance of the brain. Sometimes seizures may go unnoticed, depending on their presentation, and sometimes may be confused with other events, such as a stroke, which can also cause falls or migraines. Approximately one in every 100 persons will experience a seizure at some time in their life. Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its process is very little understood. Since its discovery by R.Caton, the Electroencephalogram (EEG) has been the most utilized signal to clinically assess brain activities. Twenty-five percent of the world's 50 million people with epilepsy have seizures that cannot be controlled by any available treatment. The need for new therapies, and success of similar devices to treat cardiac arrhythmias, has spawned an explosion of research into algorithms for use in implantable therapeutic devices for epilepsy. Most of these algorithms focus on either detecting unequivocal EEG onset of seizures or on quantitative methods for predicting seizures in the state space, time, or frequency domains that may be difficult to relate to the Neuro physiology of epilepsy. Between seizures, the EEG of a patient with epilepsy may be characterized by occasional epileptic form transients-spikes and sharp waves. EEG patterns have shown to be modified by a wide range of variables including biochemical, metabolic, circulatory, hormonal, neuro electric and behavioral factors.
Exploring various analytical approaches, both linear and non linear methods to process data from medical database is meaningful before deciding on the tool that will be most useful, accurate, and relevant for practitioners. For example, assigning a new patient to a particular outcome class is a classification problem commonly described as "pattern recognition", "discriminant analysis", and "supervised learning". In the past, the Encephalographer, by visual inspection was able to qualitatively distinguish normal EEG activity from localized or generalized abnormalities contained within relatively long EEG records. The different types of epileptic seizures are characterized by different EEG waveform patterns. With real-time monitoring to detect epileptic seizures gaining widespread recognition, the advent of computers has made it possible to effectively apply a host of methods to quantify the changes occurring based on the EEG signals. One of them is a classification of risk level of epilepsy by using Fuzzy techniques. The recognition of specific waveforms and features in the Electroencephalogram (EEG) for classification of epilepsy risk levels has been the subject of much study.
Electroencephalography is a well-established clinical procedure, which can provide information pertinent to the diagnosis of a number of brain disorders (e.g., epilepsy or brain tumors). However, despite its widespread use, it is one of the last routine clinical procedures to be fully automated. Analysis of the electroencephalogram (EEG) includes the detection of patterns and features characteristic of abnormal conditions. For example, Asymmetries in the amplitude or frequency of background activity suggest a lesion, while the presence of epileptiform activity supports a clinical diagnosis of epilepsy. Over half the EEG referrals relate to epilepsy, with the EEG being the most useful procedure in its diagnosis.
Recording the EEG during a seizure is particularly helpful in determining whether a patient has epilepsy. Because seizures usually occur infrequently and unpredictably, obtaining such recording might require an EEG extending over several days (long-term EEG monitoring). Techniques have been developed for the automated detection of petitmal seizures and grand mal seizures, which have proven relatively successful.
Between seizures, the EEG of a patient with epilepsy may be characterized by occasional epileptiform transie