Epileptic Seizures Detection Using Deep Learning Techniques Matlab

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A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for the implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.


Epilepsy is a non-communicable disease and one of the most common neurological disorders of humans, usually associated with sudden attacks . Sudden attacks of seizures are a swift and early abnormality in the electrical activity of the brain that disrupts the part or whole body. Various kinds of epileptic seizures are affecting around 60 million people worldwide. These attacks occasionally provoke cognitive disorders that can cause severe physical injury to the patient. Moreover, people with epileptic seizures sometimes suffer emotional distress due to embarrassment and lack of appropriate social status. Hence, early detection of epileptic seizures can help the patients and improve their quality of life.

The EEG signals are widely preferred as they are economical, portable, and show clear rhythms in the frequency domain. The EEG provides the voltage variations produced by the ionic current of neurons in the brain, which indicate the brain’s bioelectric activity. They need to be recorded for a long period of time to detect epileptic seizures. In addition, these signals are recorded in multiple channels, making the analysis complex. The EEG signals are also prone to artifacts generated by the main power supply, electrode movement, and muscle tremor. This will pose challenges to the physicians to diagnose epileptic seizures using noisy EEG signals. To resolve these difficulties, much research is being carried out to diagnose and predict epileptic seizures based on EEG modalities and other techniques such as MRI coupled with AI techniques. AI techniques in the field of epileptic seizures diagnosis have employed conventional machine learning and DL methods.

Many machine learning algorithms have been developed using statistical, time, frequency, time-frequency domain, and nonlinear parameters to detect epileptic seizures. In conventional machine learning techniques, the selection of features and classifiers is done by the trial-and-error method. One needs to have sound knowledge of signal processing and data mining techniques to develop an accurate model. Such models perform well for limited data. Nowadays, with the increase in the availability of data, machine learning techniques may not perform very well. Hence, the DL techniques, which are state-of-art methods, have been employed. DL models, unlike conventional machine learning techniques, require huge data in the training phase. This is because these models have a large number of feature spaces, and in case of lack of data, they face the problem of overfitting.

Proposed Method:

The main objective of this project is as follows:

  • Providing information on available EEG datasets.
  • Reviewing works done using various DL models for automated detection of epileptic seizures with various modality signals.
  • Introducing future challenges on the detection of epileptic seizures.
  • Analyzing the best performing model for various modalities of data.

Block diagram

Conclusion and future work

In recent years, a lot of research has been done in the epileptic seizures detection field using artificial intelligence methods. In this paper, a comprehensive review of works done in the field of epileptic seizure detection using various DL techniques such as CNNs, RNNs, and AEs is presented. Various screening methods have been developed using EEG and MRI modalities. We have investigated the epileptic seizures detection using DL-based practical and applied hardware methods. It is very encouraging that much of the future research will concentrate on hardware—practical applications aid in the accurate detection of such diseases. The functional hardware has also been utilized to boost the performance of detection strategies. Furthermore, the models can be placed in the cloud by hospitals. Therefore, handheld applications, mobile or wearable devices, may be equipped with such models, and cloud servers will perform the computations; by taking benefit from predictive models, these devices can be used to avert patients in a timely manner. Alert messages may be generated to the family, relatives, the concerned hospital, and doctor in the detection of epileptic seizures through the handheld devices or wearables, and thus the patient can be provided with proper treatment in time. Moreover, a cap with EEG electrodes in it can obtain the EEG signals, which can be sent to the model kept in the cloud to achieve real-time detection. Additionally, if we can detect early stage of seizure using interictal periods of EEG signals, the patient can take medication immediately and prevent seizure. This field of research requires more research that combines different screening methods for more precise and fast detection of epileptic seizures and also applies semi supervised and unsupervised methods to further overcome the dataset size limits. Finally, having publicly available comprehensive datasets can help to develop an accurate and robust model that can detect the seizure in the early stage.


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