Shannon entropy Research Papers - Academia.edu (original) (raw)

At present, manual observation of the electroencephalogram (EEG) signals is the prime method for diagnosis of epileptic seizure disorders. The method is a time consuming and error prone as it involves errors due to fatigue in continuous monitoring of nonlinear and nonstationary EEG signals. Out of approximate 1% of the world's epilepsy patients more than 25% cannot be treated correctly due to erroneous diagnosis. The automated seizure detection system can prove efficient by making the process reliable and faster. This paper reviews multi-domain feature extraction and machine learning classification techniques used in automated seizure detection systems. To analyse subtle variations in EEG, signal decomposition algorithms have been used in time, frequency, joint time-frequency, and nonlinear domain. The statistical and entropy parameters are the key features to discern normal from the seizure EEG signals. Machine learning plays a critical role in extracting meaningful information out of the extracted features. The paper also evaluates the performance of Multilayer Perceptron Neural Network, naïve Bayes, Least Square Support Vector Machine, k nearest neighbour, and random forest classifiers using sensitivity, specificity and accuracy metrics. A seizure detection technique is developed by decomposing the EEG signals by means of Tunable-Q Wavelet Transform (TQWT). To quantify the complexity of the individual multivariate sub-bands of the biomedical signals TQWT proves effective with varied values of Q factor suitable for analyzing signals with oscillatory and non-oscillatory nature. The highest accuracy of 97.3% is obtained using random forest classifier for the combination of spectral, Shannon and Kraskov entropy features. The paper compares the performance of feature extraction and classification techniques for the implemented system. The comparison explores possibility of hardware implementation of real time seizure detection scheme. 1. Introduction Epilepsy is a widespread brain disorder which affects a variety of mental and physical actions. When more than two episodes of seizures occur in a lifespan of a person then they are categorized as a seizure patient. Epileptic seizures are provoked by group of nerve cells which affect a person's normal behavior. This sudden brain signal change is life intimidating in few cases as it can cause physical injury to the affected person. In the form of partial and generalized seizures the abnormal brain activity poses a very important health concern to the patient. Partial seizures start with a specific area of brain and usually called the epileptic foci. Partial seizures may or may not affect conciseness of a person. Generalized seizures involve seizure signals originating from most part of the brain and cause loss of mental alertness and muscle spasms. The process of 'epileptogenesis' is highly unpredictable and the risk involved in the form of injury is very high (D. Buck, 1997). The seizure disorder occurs due to several causes such as birth asphyxia, stroke, traumatic brain injury or brain infections. The seizure disorders are not preventable or in some cases not completely curable but with the help of anticonvulsant drugs the life threatening seizures can be controlled in majority of the cases (Englander J., 2014). The episode of epileptic seizures occurs as the brain's controlled neonatal firing circuit malfunctions and causes excessive electrical discharge by a group of nerve cells in the brain cortex. This processing is sudden and unpredictable. Depending upon the side of cortex, out of four sides namely frontal, parietal, occipital and temporal which originates the abnormal signals, the abnormalities in the motor control results in tonic-clonic movements of muscles and joints. The discharge of electrical energy in a normal brain cells is controlled and produces variations that are in normal magnitude ranges. However an abrupt and large transient rush of energy by the brain cells results in epileptic seizures. An epileptic seizure can show variation in properties of brain waves which can result in a short term muscle movement to severe convulsions. These variations mainly depend on the area of the brain from which the energy is generated, the level of electrical energy discharge and the total area over which this energy is extended in the event of abnormal activity (Acharya U., 2013). The working of brain and its properties that cause epileptic activities are still a mystery. When a person experiences epileptic activity the possible observable signs are sudden movement of the body parts, loss of concentration, muscle involuntary movement, disturbance in visual and auditory senses and mood disorder. There can be several changes in a person suffering from mild to severe epileptic attack which are beyond the range of normal observations. When the seizures are seen in children who have limited knowledge about the situation that they experience it become difficult to notice the seizure onset. This pre-seizure behavior changes in children are linked to the behavioral disorder. Hence, children with epileptic disorder need continuous monitoring and thus the epilepsy observation is a continuous process. In order to make the process fully automated with indication of seizure occurrence many signal processing algorithms need to be considered with detailed analysis. In order to detect epilepsy using automated Computer Assisted Diagnostic (CAD) techniques using EEG signals understanding the physiological aspects of the seizure signal class is essential (Sanei, Saeid). In this work, a Tunable-Q Wavelet Transform (TQWT) (IW Selesnick, 2011) based seizure detection system is proposed which uses spectral and entropy based features to test performance of five classification algorithms. Figure 1 shows the proposed block diagram of TQWT sub-band's spectral and entropy feature based seizure classification system. As shown in figure, the features for two TQWT sub bands namely, sub-band 1 and sub-band 16 are taken to consideration for the feature extraction from normal and seizure EEG signals. The oscillatory information contained in the signal is reflected in the TQWT sub bands with low frequency content represented in the first sub band and the last sub band representing the high frequency oscillation. The EEG signal decomposition technique quantifies the sub band spectral and entropy features for low and high frequencies and this can be a widespread method to detect seizures from other EEG recording with appropriate choice of the Q-parameter. The efficacy of features extraction and classification