Enhancing EEG Signals Classification using LSTM-CNN Architecture (original) (raw)
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Automated epilepsy seizure detection from EEG signal based on hybrid CNN and LSTM model
Epilepsy is a neurological disorder that affects the normal functioning of the brain. More than 10% of the population across the globe is affected by this disorder. Electroencephalogram (EEG) is prominently employed to accumulate information about the brain's electrical activity. This study proposes an end-to-end system using a combination of two deep learning models Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTM) for the classification of EEG signals of epilepsy disordered subjects into three classes, namely preictal, normal, and seizure. The experimental results are obtained using the publicly available and popular Bonn University dataset. In this CNN-LSTM classification model the feature extraction, selection, and classification tasks are performed automatically without using handcrafted feature extraction methods. The performance of the CNN-LSTM model is examined and evaluated in terms of specificity, sensitivity, and accuracy using the tenfold cross-validation approach. The experiments performed and the obtained results show the accuracy of 99.33%, sensitivity of 99.33%, and specificity of 99.66%, respectively. Our results highlight that deep learning methods are best suited for classification in comparison to other existing state-of-the-art methods.
A LSTM-CNN Model for Epileptic Seizures Detection using EEG Signal
IEEE, 2022
Neurologists visually inspect electroencephalogram (EEG) reports to get the epilepsy diagnosis. Scholars have suggested automated techniques to detect the ailment due to the lengthy process and global shortage of specialists. Most research in the past years has been conducted utilizing machine learning methods. But following the development of deep learning methods, many groups are employing it to make computer-aided diagnostic (CAD) systems. In this work, the authors have proposed a model comprising of Convolutional Neural Network (CNN) and long short-term memory (LSTM) to detect seizures. It focuses on extracting temporal and spatial features by integrating CNN and LSTM models. The advantage of this automated system is it extracts spatial as well as temporal features from EEG signals with less trainable parameters and gives good accuracy. This makes the system suitable for real-time processing applications. It has achieved a maximum of 100% accuracy, 100% sensitivity, and 100% specificity to distinguish between healthy and seizure patients.
Long Short-Term Memory (LSTM) based Epileptic Seizure Recognition
International Journal of Computer Applications
Epilepsy is the second most common brain disorder after migraine; automatic detection of epileptic seizures can considerably improve the patients" quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter many challenges in real-life situations; EEG data are prone to numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address this challenge, we propose a deep learning-based approach that learns the discriminative EEG features of epileptic seizures and to distinguish between the different types of patient recordings. More specifically, we aim to tackle this issue by using a Long Short-Term Memory network, and explore the capabilities of this model.
Lecture notes in networks and systems, 2022
Epilepsy is a type of neurological brain disorder due to a temporary change in the brain's electrical function. If diagnosed and treated, there can be no seizures. Electroencephalogram (EEG) is the most common technique used in diagnosing epilepsy to avoid danger and take preventive precautions. This paper applied deep learning and machine learning techniques for detecting epileptic seizures and identifying whether machine learning or deep learning classifiers are more pertinent for the purpose and then trying to improve the present techniques for seizure detection. The best performance of the deep learning models has been achieved by implementing the convolutional neural network (CNN) algorithm on the EEG signal dataset in which the result appears as follows: accuracy 99.2%, specificity 99.3% and sensitivity 98.7%. For hybrid deep neural network CNN with long short-term memory (LSTM), the accuracy reached is 98.7%.
Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure
Diagnostics
Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder—Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our ap...
Epileptic Seizure Detection using Deep Learning Approach
UHD Journal of Science and Technology
An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities. Many methods have been developed to help the neurophysiologists to detect the seizure activities with high accuracy. Most of them rely on the features extracted in the time, frequency, or time-frequency domains. The performance of the proposed methods is related to the performance of the features extracted from EEG recordings. Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been hugely successful in almost all machine learning applications. We propose a new framework that also learns directly from the data, without extracting a feature set. We proposed an original deep-learning-based method to classify EEG recordings. The EEG signal is segmented into 4 s segments and used to train the long- and short-term memory ne...
Automated Deep Neural Network Approach for Detection of Epileptic Seizures
In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data ...
Multidimensional CNN and LSTM for Predicting Epilepsy Seizure Activities
International Journal of Advanced Scientific Innovation (IJASI), 2023
Epilepsy is a chronic neurological disease caused by sudden abnormal brain discharges, leading to temporary brain dysfunction. It can manifest in various ways, including paroxysmal movement, sensory, autonomic nerve, awareness, and mental abnormalities. It is now the second largest neurological disorder worldwide, affecting around 70 million people and increasing by approximately 2 million new cases each year. While about 70% of epilepsy patients can control their seizures with regular antiepileptic drugs, surgery, or nerve stimulation treatments, the remaining 30% suffer from intractable epilepsy without effective treatment, causing significant burden and potential danger to their lives. Early prediction and treatment are crucial to prevent harm to patients. Electroencephalogram (EEG) is a valuable tool for diagnosing epilepsy as it records the brain's electrical activity. EEG can be divided into scalp and intracranial types, and doctors typically analyze EEG signals of epileptic patients into four periods.
Prediction of the epileptic seizure through deep learning techniques using electroencephalography
The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023
Electroencephalography (EEG) is a widely used and significant technique for aiding in epilepsy diagnosis and investigating the electrical patterns of the human brain. Due to the non-stationary nature of EEG signals, seizure patterns will vary across different recording sessions for individual patients. In this study, a new deep learning long short-term memory (LSTM) model is implemented for the detection of brain tumors and seizures. The process consists of four key steps: EEG signal pre-processing, preictal feature extraction, hyper optimization using grey wolf optimization (GWO), and LSTM-based classification. The evaluation makes use of long-term EEG recordings from the EEG and ABIDE fMRI datasets. By experimenting with various modules and layers of memory units, a pre-analysis is first conducted to determine the best LSTM network architecture. The LSTM model makes use of numerous retrieved features, including temporal and frequency domain information between EEG channels that were extracted before classification. The discovery of the implemented methodology revealed significant advantages in accurately predicting seizures while minimizing false alarms. The implemented LSTM method achieves a 99% accuracy rate, 98% precision, 99% recall, and 98% f1-measure which is better when compared with cross sub-pattern correlation-based principal component analysis (SUBXPCA) and gradient-boosting decision tree (GBDT) methods.
MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG
Biomedical Signal Processing and Control
Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism. The objective of this study was to classify specific types of seizures, including complex partial, simple partial, absence, tonic, and tonic-clonic seizures, using only electroencephalogram (EEG) data. The EEG data is fed to our proposed model in two different representations. The CNN was fed with wavelet-based features extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals to let our MP-SeizNet jointly learns from different representations of seizure data for more accurate information learning. The proposed MP-SeizNet was evaluated using the largest available EEG epilepsy database, the Temple University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed model across different patient data using threefold cross-validation and across seizure data using five-fold cross-validation, achieving F1-scores of 87.6% and 98.1%, respectively.