مقاله انگلیسی رایگان در مورد شبکه عصبی مرکزی عمیق برای تشخیص تشنج – الزویر 2018

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.

INTRODUCTION

According to the World Health Organization (WHO), nearly 50 million people suffer from epilepsy worldwide [1]. It is estimated that 2.4 million people are diagnosed with epilepsy annually [1]. Seizures are due to the uncontrolled electrical discharges in a group of neurons. [2, 3]. The excessive electrical discharges result in the disruption of brain function. Epilepsy is diagnosed when there is recurrence of at least two unprovoked seizures. It can affect anyone at any age [4]. A timely and accurate diagnosis of epilepsy is essential for patients in order to initiate anti-epileptic drug therapy and subsequently reduce the risk of future seizures and seizurerelated complications [5]. Currently, the diagnosis of epilepsy is made by obtaining a detailed history, performing a neurological exam, and ancillary testing such as neuro-imaging and EEG. The EEG signals can identify inter-ictal (between seizures) and ictal (during seizure) epileptiform abnormalities. Figure 1 shows a graphical representation of the electrical activity in the brain of healthy subjects and seizure patients. Typically, neurons communicate through electrical signals. Therefore, in a regular brain activity, these electrical signals are normally regulated [3] (see the normal activity in Figure 1). However, during seizure, there is an abnormally increased hypersynchronous electrical activity of epileptogenic neural network. This activity may remain localized to one part of the brain, or spread to the entire brain. In either scenario, an individual may experience a clinical seizure (see the seizure activity in Figure 1) [3]. Neurologists scrutinize the EEG via direct visual inspection to investigate for epileptiform abnormalities that may provide valuable information on the type and etiology of a patient’s epilepsy. However, interpretation of the EEG signals by visual assessment is time-consuming particularly with the increased use of out-patient ambulatory EEG’s and in-patient continuous video EEG recordings, where there are hours or days worth of EEG data that needs to be reviewed manually [6]. The majority of EEG software includes some form of automated seizuredetection, however, due to the poor sensitivity and specificity of the pre-determined seizure detection algorithms, the current forms of automated seizure detection are rarely used in clinical practice. In addition, the inherent nature of visual inspection results in varying clinical interpretations based on the EEG reader’s level of expertise in electroencephalography. Complicating matters, the quality of the study may be confounded by interfering artifactual signal limiting the reader’s ability to accurately identify abnormalities. Moreover, the low yield of routine out-patient studies poses another problem. A patient with epilepsy can go for an outpatient EEG and the study may be completely normal. This is because the brains of patients with epilepsy are generally not continually firing off epileptic discharges.