In the USA, epilepsy is considered as the second most popular neurological condition and is known to affect individuals over the age of 65 more, with over one percent of Americans experiencing epilepsy every year. The ability to detect epilepsy from EEG signals is vital in treating epilepsy and in the prediction of seizures. The different seizure types usually produce distinct spikes on the EEG signals gotten from the brain (Sanei &
Chambers 2007). Lot of efforts have gone into detecting epilepsy spikes from EEG signals (Haydari et al. 2011; Schuyler et al. 2007)
EEG signals were first reported by German neuropsychiatrist Hans Berger in the early 1920s, these signals keep record of the electrical activities in the brain and they have primarily become a good alternative in the diagnosis of neurological disorders. Through the analysis of these EEG recordings some vital information about the physiological nature of the brain can be gotten and these could prove important in the detection of epilepsy, because the occurrence of seizures show obvious abnormalities in the EEG signals. Therefore, an impending seizure attack could be managed properly to avoid any inherent danger to the individual by initiating a warning signal (Zainuddin et al. 2013).
The automated detection of seizures is still a challenge because false positives are generated and due to this, the accuracy of automated EEGs are not trusted in clinical settings. Time series model are better suited due to the nature of waveform data gotten from EEG. Due to the fact the data gotten is shown as continuous wave forms, this allows specialists to be able to analyze, detect seizures and also identify other brain activities from the EEG readings, the data gotten by the machine is usually numerous electrical discrete readings that is measured in millivolts (mV) (Turner et al. 2014).
The diagnosis of epilepsy is made when an individual suffers an epileptic seizure and they have a condition that puts them at risk of encountering another one. Usually, the electrical information and activities generated by the cerebral nerve cortex of the brain is usually recorded by an electroencephalogram (EEG). These EEG signals are regarded as one of the vital physiological signals that can be used in the detection of epilepsy (Sadati et al.
2006). Although, the visual inspection of recordings gotten from EEG for epileptic related features is tedious and time consuming, and bio-signals may vary and show disagreements regarding the same condition. (Sadati et al. 2006; Mohamed et al. 2012).
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Electroencephalography (EEG) is a popular technique that is used in the detection of epileptic seizure. It works by detecting electrical impulses generated by the brain neurons and detected by simple electrodes attached to the scalp (Wang & Zhang 2009). By placing these electrodes on the scalp, the brain activity is recorded with emphasis on the abnormal differences in the voltage impulses produced by the brain (Niedermeyer et al. 2005). EEG can provide valuable insight into the problems associated with the brain and provide details about the brain activity. The seizure free periods in epileptic patients has been considered as a vital part in the in the prediction and diagnosis process of epilepsy ( Alkan et al 2005; Adeli et al. 2009;Oğulata et al. 2009;Abualsaud et al. 2014;).
The analysis of epileptic seizure is based on the visual identification gotten from EEG signals and is performed by experienced neurologists who look for patterns of interest like spikes or spike wave discharges. This process of analysis is time consuming and requires expertise and often times cause disagreements between neuroscientist because the signal analysis is subjective.(Mohseni et al. 2006; Mporas et al., 2014)
Epileptic seizures could either affect a part of the brain (partial seizure) these are seen in few channels of the EEG recording or the entire brain which is seen in all channels of an EEG recording (Gotman 1999). It is the daily practice of neurologist to examine short recordings of interictal periods. The individual or isolated spikes, the sharp wave and spike-and-wave complex are the most common interictal periods. These periods are usually seen in most people with epilepsy and this makes the detection of interictal event important in diagnosing epilepsy. Although, during an isolated spike the brain is not in clinical seizure. During ictal period different EEG patterns is seen which consists of waveforms of different frequencies. Though interictal findings provide proof of epilepsy , diagnosing epilepsy is based on the observation of epileptic seizures (Gotman 1999;
McGrogan 1999;Tzallas, Tsipouras and Fotiadis, 2009)
According to Siddiqui & Islam (2016) signals from electrocorticogram (ECoG) are used in the monitoring of brain signals, the process by which EEG electrodes are attached to the scalp of the brain in an invasive manner is termed as ECog. These signals gotten are time series in nature, they are of importance in seizure detection and intervention. These time series data are surveillance made, with the sequence of time. The main feature of
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these time series data is their high dimensionality and size which is calculated with respect to time (Fu 2011;Siddiqui & Islam 2016).
The presence of interictal spikes is generally accepted as a sign of epilepsy, although the reasons of the presence of interictal activity in the brain are unknown (Staley & Dudek, 2006). Apart from the usual activity recorded in the brain during epileptic seizures, the EEG of epileptic patients will show ‘spikes’ in some locations in the brain. These spikes provide information in the localization of epilepsy and in its diagnosis (Valenti et al.
2006).
When preparing an epileptic patient for epilepsy surgery, long periods of video/EEG recording of both the interictal and ictal periods of epilepsy are analyzed to decide the localization of the epileptogenic zone. The method to automatically detect interictal spikes has been used for many years to improve the visual analysis of large numbers of data. Different attempts have been made to determine a proper spike detection method but they have all come short due to the lack of characterization of the events to detect (Valenti et al. 2006).
Due to the recent interest and promise shown by data mining methods, data mining computational models can be used to extract recently unknown and useful information from large databases. The main rationale behind these is the methods is in the discovery of patterns that can be found in large databases which are hidden at first glance due to large amount of data stored ( Mitchell 1997; Flexer 2000;Valenti et al. 2006).
The two important steps in the automated detection of epileptic EEG involves the feature extraction method and the classification method. The features used in the automatic detection of EEG fall into four main categories which are time domain, frequency domain, time-frequency domain, and nonlinear domain (Thakor et al. 2004; Wang et al. 2014)
35 4 AIMS OF THE STUDY
The aim of this study is to explore the use of different data mining methods for epilepsy seizure detection and improved clinical decision support.
The objectives of this scoping review are:
1) To identify the use of data mining methods in the analysis of data on epilepsy.
2) To highlight the data mining methods used in the detection of epilepsy.
3) To understand the role of seizure detection in improved clinical decision support
36 5 METHODOLOGY