Study of Learning Entropy for onset detection of epileptic seizures in EEG time series (original) (raw)
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Detection of epileptic seizure based on entropy analysis of short-term EEG
PloS one, 2018
Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data leng...
An Entropy-based Feature in Epileptic Seizure Prediction Algorithm
Epilepsy prediction is a vital demand for people suffering from epileptic onset. Prediction of seizure onsets could be very useful for drug-resistant epileptic patients. We propose an epileptic seizure prediction algorithm to predict an onset of epilepsy and discriminate between pre-seizure periods and seizure free periods. The proposed algorithm is based on entropy features of 60 (1 hour segmented into 60 periods) with free seizure periods and repeated for 24 hour, and 60 (pre-seizure periods) of the CHB-MIT Scalp EEG Database (Female less or equal 12 age). Critical values of the sample entropy and approximate entropy are estimated to locate starting of the seizure onset. These values are taken as warning to a probably seizure starts within a specific time. The prediction time in order of 1min-49min is achieved in 60 seizure periods under study in this task. SVM is used to classify pre-seizure periods from seizure free periods for the mentioned data. The performance is evaluated and analysed.
Epilepsy is associated with the abnormal electrical activity in the brain which is detected by recording EEG (Electroencephalogram) signals. This signal is non-linear and chaotic and hence, it is very time-consuming and tedious to analyse them visually. In this work, we have extracted five entropy features such as Approximate Entropy, Sample Entropy, Fuzzy Entropy, Permutation Entropy and Multi-scale Entropy for characterizing the focal signals. We have used Sequential Forward Feature Selection (SFFS) algorithm to select two significant features for epilepsy classification. These two features are given as input to the Least Square Support Vector Machine (LS-SVM) classifier to differentiate normal and focal signal. The classification accuracy of our method is 82%. Moreover, the average computational time for the selected feature set is 47.94 seconds.
An Efficient Epileptic Seizure Detection Using Entropy Features with Optimal Neural Network
2020
The planning of electroencephalogram (EEG) signal location is a difficult errand because of the need of removing delegate designs from multidimensional time arrangement created from EEG estimations. Productively recognizing epileptic seizure EEG signals is helpful in taking care of neurological variations from the norm and furthermore in assessment of the physiological condition of the mind for a wide scope of utilizations in the field of biomedical. The electrical activity of the brain is indicated by the EEG signals and also it contains useful information about the state of the brain for studying brain function. In the manual scoring there is always a chance for human errors, also it consumes a lot of time, process is costly and not sufficient enough for reliable information. This developed a need of designing an automatic system for evaluating and diagnosing epileptic seizure EEG signals to eliminate the chance of the analyst missing data. An Adaptive artificial neural network (A...
An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009
We describe a novel algorithm for the prediction of epileptic seizures using scalp EEG. The method is based on the analysis of the positive zero-crossing interval series of the EEG signal and its first and second derivatives as a measure of brain dynamics. In a moving-window analysis, we estimated the probability density of these intervals and computed the differential entropy. The resultant entropy time series were then inspected using the cumulative sum (CUSUM) procedure to detect decreases as precursors of upcoming seizures. In the next step, the alarm sequences resulting from analysis of the EEG waveform and its derivatives were combined. Finally, a seizure prediction index was generated based on the spatio-temporal processing of the combined CUSUM alarms. We evaluated our algorithm using a dataset of approximately 21.5 hours of multichannel scalp EEG recordings from four patients with temporal lobe epilepsy, resulting in 87.5% sensitivity, a false prediction rate of 0.28/hr, an...