# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 1 Introduction  
Epilepsy as a neurological condition impacts over 50 million individuals globally [1], which is characterized by abnormal increases in brain electrical activity, leading to symptoms such as loss of attention, hallucinations, and convulsions [2]. The consequences of epileptic seizures can be severe, impacting physical health, mental well-being, and social relationships. These effects include loss of consciousness, potential injuries, and, in extreme cases, even the risk of sudden death. The acquisition of epileptic episodes through electroencephalogram (EEG) provides real-time, cost-effective, and non-invasive information with exceptional spatio-temporal resolution [3]. Detecting seizures in EEG signals playsa critical role in diagnosing and treating epilepsy. Depending on the extent of brain area involvement during a seizure, epilepsy can be classified into two main types: focal seizures, which affect specific brain areas, and generalized seizures, which involve both sides of the brain. The neuronal signals, originating from brain activity, are captured by electrodes positioned on the scalp's surface. [4]. These electrodes are placed on the scalp based on the configuration proposed in the international 10-20 system of electrode placement [5], as depicted in Figure 1. Electrodes are identified based on their locations: frontal polar (Fp), frontal (F), central (C), parietal (P), temporal (T), occipital (O), and auricular (A), with even numbers on the right hemisphere and odd numbers on the left hemisphere, and with \"z\" denoting the midline. Additionally, electrodes such as nasion (nose), inion (nape), and auricular points are employed as reference points. EEG signals belong to the complex domain, wherein each channel associated with an electrode measures the sum of electrical impulses from the cerebral cortex, originating from the activity of billions of neurons proximal to the electrode [6]. EEG signals provide temporal, spatial, and spectral information. The frequency range of EEG signals typically varies between 0.5 and 100 Hz, delineating five principal bands associated with brain and body states, identified by the Greek letters delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz) [7]. During an epileptic seizure, hypersynchronization of nervous signals leads to a significant increase in signal amplitude across the bandwidth. This increase is observable in three phases: preictal, ictal, and interictal, corresponding to the periods preceding the seizure, during the seizure, and the period between two attacks, respectively [8]. Figure 2 shows an EEG signal with an epileptic seizure occurring in the interval 2996-3036 s, as annotated by neurophysiologists. The beginning and end of the seizure period were delineated, along with the respective preictal and postictal periods. Classifying EEG signals presents a significant challenge due to the dynamic nature of the signals and the diverse patterns of seizures observed across patients and recording  
Figure 1: International 10–20 System. Figure 2: EEG signal with epileptic seizure interval. sessions. Additionally, EEG data acquisition systems encounter obstacles due to their susceptibility to various forms of interference, for example, muscle movements, blinking, and ambient background noise. While visual analysis of EEG signals can identify epileptic crises, this manual process is slow, costly, and prone to human errors. The increasing demand for more accurate and efficient diagnoses has prompted researchers to develop algorithms for the automatic detection of these crises. This study introduces a model for detecting epileptic attacks in EEGs based on the classification of alpha and beta brainwaves during intervals with and without crises. In this study, a Savitzky-Golay filter is employed to increase the signal-to-noise ratio (SNR) and alpha and beta waves are derived through the application of discrete wavelet transform (DWT).