The Experts below are selected from a list of 26154 Experts worldwide ranked by ideXlab platform
Abdulhamit Subasi - One of the best experts on this subject based on the ideXlab platform.
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Epileptic Seizure detection using hybrid machine learning methods
Neural Computing and Applications, 2019Co-Authors: Abdulhamit Subasi, Jasmin Kevric, Abdullah M CanbazAbstract:The aim of this study is to establish a hybrid model for Epileptic Seizure detection with genetic algorithm (GA) and particle swarm optimization (PSO) to determine the optimum parameters of support vector machines (SVMs) for classification of EEG data. SVMs are one of the robust machine learning techniques and have been extensively used in many application areas. The kernel parameter’s setting for SVMs in training process effects the classification accuracy. We used GA- and PSO-based approach to optimize the SVM parameters. Compared to the GA algorithm, the PSO-based approach significantly improves the classification accuracy. It is shown that the proposed Hybrid SVM can reach a classification accuracy of up to 99.38% for the EEG datasets. Hence, the proposed Hybrid SVM is an efficient tool for neuroscientists to detect Epileptic Seizure in EEG.
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The Effect of Multiscale PCA De-noising in Epileptic Seizure Detection
Journal of Medical Systems, 2014Co-Authors: Jasmin Kevric, Abdulhamit SubasiAbstract:In this paper we describe the effect of Multiscale Principal Component Analysis (MSPCA) de-noising method in terms of Epileptic Seizure detection. In addition, we developed a patient-independent Seizure detection algorithm using Freiburg EEG database. Each patient contains datasets called “ictal” and “interictal”. Window length of 16 s was applied to extract EEG segments from datasets of each patient. Furthermore, Power Spectral Density (PSD) of each EEG segment was estimated using different spectral analysis methods. Afterwards, these values were fed as input to different machine learning methods that were responsible for Seizure detection. We also applied MSPCA de-noising method to EEG segments prior to PSD estimation to determine if MSPCA can further enhance the classifiers’ performance. The MSPCA drastically improved both the sensitivity and the specificity, increasing the overall accuracy of all three classifiers up to 20 %. The best overall detection accuracy (99.59 %) was achieved when Eigenvector analysis was used for frequency estimation, and C4.5 as a classifier. The experiment results show that MSPCA is an effective de-noising method for improving the classification performance in Epileptic Seizure detection.
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application of adaptive neuro fuzzy inference system for Epileptic Seizure detection using wavelet feature extraction
Computers in Biology and Medicine, 2007Co-Authors: Abdulhamit SubasiAbstract:Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for Epileptic Seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of Epileptic Seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in Epileptic Seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.
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automatic detection of Epileptic Seizure using dynamic fuzzy neural networks
Expert Systems With Applications, 2006Co-Authors: Abdulhamit SubasiAbstract:Abstract In this study, a new approach based on neural network and fuzzy logic technologies was presented for detection of Epileptic Seizure to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A dynamic fuzzy neural network (DFNN) that contains dynamical elements in their processing units is used in the classification of EEG signals. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a DFNN with two discrete outputs: normal and Epileptic. Some conclusions concerning the impacts of features on Epileptic Seizure detection was obtained through analysis of the DFNN. The performance of the DFNN model was evaluated in terms of classification accuracies and the results confirmed that the proposed DFNN classifiers have some potential in detecting Epileptic Seizures. The DFNN model achieved accuracy rates, which were higher than that of neural network model.
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Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure
Journal of Medical Systems, 2004Co-Authors: M. Kemal Kiymik, Abdulhamit Subasi, H. Rıza OzcalıkAbstract:Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing Epileptic disorders. Predicting the onset of Epileptic Seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of Epileptic Seizures in EEG, allowing the grouping of Seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the Seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group Seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of Epileptic Seizure.
Yu Zhang - One of the best experts on this subject based on the ideXlab platform.
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adversarial representation learning for robust patient independent Epileptic Seizure detection
IEEE Journal of Biomedical and Health Informatics, 2020Co-Authors: Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu ZhangAbstract:Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous Seizures, which affects about one percent of the worlds population. Most of the current Seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable Epileptic Seizure detection model that effectively learns from Seizure states while eliminates the inter-patient noises. A complex deep neural network model is proposed to learn the pure Seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the Seizure diagnosis procedure. The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. We propose an effective and efficient patient-independent diagnosis approach of Epileptic Seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.
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Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
2020Co-Authors: Zhang Xiang, Yu Zhang, Yao Lina, Dong Manqing, Liu Zhe, Li YongAbstract:Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous Seizures, which affects about one percent of the world's population. Most of the current Seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable Epileptic Seizure detection model that effectively learns from Seizure states while eliminates the inter-patient noises. Methods: A complex deep neural network model is proposed to learn the pure Seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the Seizure diagnosis procedure. Results: The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. Conclusion and significance: We propose an effective and efficient patient-independent diagnosis approach of Epileptic Seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.Comment: Accepted by the IEEE Journal of Biomedical and Health Informatics (J-BHI
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adversarial representation learning for robust patient independent Epileptic Seizure detection
arXiv: Signal Processing, 2019Co-Authors: Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu ZhangAbstract:Objective: Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous Seizures, which affects about one percent of the world's population. Most of the current Seizure detection approaches strongly rely on patient history records and thus fail in the patient-independent situation of detecting the new patients. To overcome such limitation, we propose a robust and explainable Epileptic Seizure detection model that effectively learns from Seizure states while eliminates the inter-patient noises. Methods: A complex deep neural network model is proposed to learn the pure Seizure-specific representation from the raw non-invasive electroencephalography (EEG) signals through adversarial training. Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the Seizure diagnosis procedure. Results: The proposed approach is evaluated over the Temple University Hospital EEG (TUH EEG) database. The experimental results illustrate that our model outperforms the competitive state-of-the-art baselines with low latency. Moreover, the designed attention mechanism is demonstrated ables to provide fine-grained information for pathological analysis. Conclusion and significance: We propose an effective and efficient patient-independent diagnosis approach of Epileptic Seizure based on raw EEG signals without manually feature engineering, which is a step toward the development of large-scale deployment for real-life use.
Rajendra U Acharya - One of the best experts on this subject based on the ideXlab platform.
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mmsfl owfb a novel class of orthogonal wavelet filters for Epileptic Seizure detection
Knowledge Based Systems, 2018Co-Authors: Manish Sharma, Ankit A Bhurane, Rajendra U AcharyaAbstract:Abstract The optimal filters with minimal bandwidth are highly desirable in many applications such as communication and biomedical signal processing. In this study, we design optimally frequency localized orthogonal wavelet filters and evaluate their performance using electroencephalogram (EEG) signals for automated detection of the Epileptic Seizure. The paper presents a novel method for designing optimal orthogonal wavelet filter banks (OWFB) with the objective of minimizing their frequency spreads. The designed wavelet filter also possesses the desired degree of regularity. The regularity condition has been imposed analytically so as to satisfy the constraint accurately. We propose a novel semi-definite programming (SDP) formulation which does not involve any parametrization. The solution of the SDP yields optimal orthogonal wavelet filter for the given length of the filter. We have developed an automated diagnosis system that identifies Epileptic Seizure EEG signals using the features obtained from the designed minimally mean squared frequency localized (MMSFL) OWFB. We have tested the performance of the proposed model using two independent EEG databases in order to ensure the consistency and robustness of the model. Interestingly, the proposed MMSFL-OWFB feature-based model exhibits ceiling level of performance, with classification accuracy ≥ 99% in classifying Seizure (ictal) and Seizure-free (non-ictal) EEG signals for both databases. Our developed system can be employed in hospitals and community cares to aid the epileptologists in the accurate diagnosis of Seizures.
Varun Bajaj - One of the best experts on this subject based on the ideXlab platform.
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Epileptic Seizure detection based on the instantaneous area of analytic intrinsic mode functions of eeg signals
Biomedical Engineering Letters, 2013Co-Authors: Varun Bajaj, Ram Bilas PachoriAbstract:Epileptic Seizure is generated by abnormal synchronization of neurons of the cerebral cortex of the patients, which is commonly detected by electroencephalograph (EEG) signals. In this paper, the intracranial EEG signals have been used to detect focal temporal lobe epilepsy. This paper presents a new method based on empirical mode decomposition (EMD) of EEG signals for detection of Epileptic Seizures. The proposed method uses the Hilbert transformation of intrinsic mode functions (IMFs), obtained by EMD process that provides analytic signal representation of IMFs. The instantaneous area measured from the trace of the windowed analytic IMFs of EEG signals provides rules-based detection of focal temporal lobe epilepsy. The experiment results on intracranial EEG signals are included to show the effectiveness of the proposed method for detection of focal temporal lobe epilepsy. The performance evaluation of the proposed method for Epileptic Seizure detection has performed by computing the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and error rate detection (ERD). The proposed method has been compared to the existing methods for detecting focal temporal lobe epilepsy from intracranial EEG signals. The proposed method has provided detection of focal temporal lobe epilepsy with increased accuracy.
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analysis of normal and Epileptic Seizure eeg signals using empirical mode decomposition
Computer Methods and Programs in Biomedicine, 2011Co-Authors: Ram Bilas Pachori, Varun BajajAbstract:Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the Epileptic Seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.
Dang Khoa Nguyen - One of the best experts on this subject based on the ideXlab platform.
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a novel low power implantable Epileptic Seizure onset detector
IEEE Transactions on Biomedical Circuits and Systems, 2011Co-Authors: M T Salam, Mohamad Sawan, Dang Khoa NguyenAbstract:A novel implantable low-power integrated circuit is proposed for real-time Epileptic Seizure detection. The presented chip is part of an epilepsy prosthesis device that triggers focal treatment to disrupt Seizure progression. The proposed chip integrates a front-end preamplifier, voltage-level detectors, digital demodulators, and a high-frequency detector. The preamplifier uses a new chopper stabilizer topology that reduces instrumentation low-frequency and ripple noises by modulating the signal in the analog domain and demodulating it in the digital domain. Moreover, each voltage-level detector consists of an ultra-low-power comparator with an adjustable threshold voltage. The digitally integrated high-frequency detector is tunable to recognize the high-frequency activities for the unique detection of Seizure patterns specific to each patient. The digitally controlled circuits perform accurate Seizure detection. A mathematical model of the proposed Seizure detection algorithm was validated in Matlab and circuits were implemented in a 2 mm2 chip using the CMOS 0.18- μm process. The proposed detector was tested by using intracerebral electroencephalography (icEEG) recordings from seven patients with drug-resistant epilepsy. The Seizure signals were assessed by the proposed detector and the average Seizure detection delay was 13.5 s, well before the onset of clinical manifestations. The measured total power consumption of the detector is 51 μW.