14 research outputs found
An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms
Objectives: Electroencephalogram (EEG) signal gives a viable perception about the neurological action of the human brain that aids the detection of epilepsy. The objective of this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops a computer-aided diagnosis (CAD) machine learning model which can spontaneously classify pre-ictal and ictal EEG signals. In the proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) and Efficient Particle Swarm Optimization (EPSO) are used to determine the optimum parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared to the FFA and EPSO algorithm separately, the composite (ANFIS+FFA+EPSO) optimization algorithm outperforms in all respects. The proposed technique achieved accuracy, specificity, and sensitivity of 99.87%, 98.71% and 100% respectively. Conclusion: The ANFIS-FFA-EPSO method is able to enhance the seizure detection outcomes for demand forecast in hospital
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification
Identifying seizure activities in non-stationary electroencephalography (EEG)
is a challenging task, since it is time-consuming, burdensome, and dependent on
expensive human resources and subject to error and bias. A computerized seizure
identification scheme can eradicate the above problems, assist clinicians and
benefit epilepsy research. So far, several attempts were made to develop
automatic systems to help neurophysiologists accurately identify epileptic
seizures. In this research, a fully automated system is presented to
automatically detect the various states of the epileptic seizure. The proposed
method is based on sparse representation-based classification (SRC) theory and
the proposed dictionary learning using electroencephalogram (EEG) signals.
Furthermore, the proposed method does not require additional preprocessing and
extraction of features which is common in the existing methods. The proposed
method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9
scenarios. It is also robust to the measurement noise of level as much as 0 dB.
Compared to state-of-the-art algorithms and other common methods, the proposed
method outperformed them in terms of sensitivity, specificity and accuracy.
Moreover, it includes the most comprehensive scenarios for epileptic seizure
detection, including different combinations of 2 to 5 class scenarios. The
proposed automatic identification of epileptic seizures method can reduce the
burden on medical professionals in analyzing large data through visual
inspection as well as in deprived societies suffering from a shortage of
functional magnetic resonance imaging (fMRI) equipment and specialized
physician
An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach
Epilepsy is a neurological disorder and for its detection, encephalography
(EEG) is a commonly used clinical approach. Manual inspection of EEG brain
signals is a time-consuming and laborious process, which puts heavy burden on
neurologists and affects their performance. Several automatic techniques have
been proposed using traditional approaches to assist neurologists in detecting
binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal.
These methods do not perform well when classifying ternary case e.g. ictal vs.
normal vs. inter-ictal; the maximum accuracy for this case by the
state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a
system based on deep learning, which is an ensemble of pyramidal
one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model,
the bottleneck is the large number of learnable parameters. P-1D-CNN works on
the concept of refinement approach and it results in 60% fewer parameters
compared to traditional CNN models. Further to overcome the limitations of
small amount of data, we proposed augmentation schemes for learning P-1D-CNN
model. In almost all the cases concerning epilepsy detection, the proposed
system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page
Effect of Data Preprocessing in the Detection of Epilepsy using Machine Learning Techniques
1066-1077Epilepsy is the one of the most neurological disorder in our day to day life. It affects more than seventy million people
throughout the world and becomes second neurological diseases after migraine. Manual inspection of seizures is time
consuming and laborious task. Nowadays automated techniques are evolved for detection of seizures by means of signal
processing or through machine learning techniques. In this article, supervised learning algorithms are applied to the EEG dataset
and performance are measured in terms of Accuracy, precision and few more. Machine learning algorithm plays a vital role in
classification and regression problem in the past few decades. The most important reason for this is a large set of signal or data
are trained and the test signals are evaluated using training network. To get the better accuracy, the input data are first
normalized carefully. The various normalization techniques applied in this article are Z-Score, Min-Max, Logarithmic and
Square Root Normalization. For simulation purpose, Electroencephalography (EEG) signal from UCI Machine Learning
Respiratory are used. Dataset consists of 11500 patient details with 5 different cases and each signal are recorded for the
duration of 23 seconds. Spider chart is used to show the metric value in detail. It is observed from the result that supervised
learning algorithm yields a better result compared to logistic and KNN (K-Nearest Neighbor) algorithm at high iteration
Residual and bidirectional LSTM for epileptic seizure detection
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches