In this study, a new scheme was presented for detecting epileptic seizures from electro-encephalo-gram (EEG) data recorded from normal subjects and epileptic patients. The new scheme was based on approximate entropy (ApEn) and discrete wavelet transform (DWT) analysis of EEG signals. Seizure detection was accomplished in two stages. In the first stage, EEG signals were decomposed into approximation and detail coefficients using DWT. In the second stage, ApEn values of the approximation and detail coefficients were computed. Significant differences were found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with over 96% accuracy. Without DWT as preprocessing step, it was shown that the detection rate was reduced to 73%. The analysis results depicted that during seizure activity EEG had lower ApEn values compared to normal EEG. This suggested that epileptic EEG was more predictable or less complex than the normal EEG. The data was further analyzed with surrogate data analysis methods to test for evidence of nonlinearities. It was shown that epileptic EEG had significant nonlinearity whereas normal EEG behaved similar to Gaussian linear stochastic process.

Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t- f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.

Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis

Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform

Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform by visual inspection, thus opening up a vast research area for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. Moreover, the EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Hence, it is necessary to develop a Computer Aided Diagnostic (CAD) system to automatically identify the normal and abnormal activities using minimum number of highly discriminating features in classifiers. It has been found that nonlinear features are able to capture the complex physiological phenomena such as abrupt transitions and chaotic behavior in the EEG signals. In this review, we discuss various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail. We also briefly present the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting.

Automated EEG analysis of epilepsy: A review

https://bcmi.sjtu.edu.cn/~blu/papers/2014/2.PDF

Emotional state classification from EEG data using machine learning approach

Entropies for detection of epilepsy in EEG

Characterization of EEG-A comparative study

Cardiac state diagnosis using adaptive neuro-fuzzy technique.

Erratum to "Entropies for detection of epilepsy in EEG" (Comput. Methods Prog. Biomed. 80 (2005) 187-194)

The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details can not be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using Artificial Neural Network (ANN), Fuzzy relations and statistical classifier. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for Fuzzy classifier and statistical classifiers. The Fuzzy classifier and statistical classifiers are seen to be correc...

P. K. Sadasivan

Papers

Entropies for detection of epilepsy in EEG

Characterization of EEG-A comparative study

Cardiac state diagnosis using adaptive neuro-fuzzy technique.

Erratum to "Entropies for detection of epilepsy in EEG" (Comput. Methods Prog. Biomed. 80 (2005) 187-194)

Cardiac Health Diagnosis Using Heart Rate Variability Signals – A Comparative Study