Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2007.01276v3)

Abstract: 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 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.

• The paper provides a comprehensive survey of deep learning methods for automated epileptic seizure detection using EEG and MRI modalities.
• The paper examines various architectures, such as CNNs and RNNs, and emphasizes robust preprocessing of EEG data to reduce noise.
• The paper identifies challenges like dataset scarcity and real-time constraints while outlining future directions for wearable and cloud-based seizure monitoring.

Overview of "Epileptic Seizures Detection Using Deep Learning Techniques: A Review"

The paper under discussion presents a comprehensive review of deep learning (DL) techniques employed for the automated detection of epileptic seizures, primarily utilizing electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. The work is authored by Afshin Shoeibi et al., and it aims to provide an extensive survey of the methodologies, challenges, and future directions of using DL in the context of epileptic seizure detection.

Summary of Key Sections

1. Introduction and Background: The paper outlines epilepsy as a prevalent neurological disorder that necessitates effective seizure detection strategies. Traditional methods for diagnosing seizures have relied heavily on manual examination by neurologists, posing challenges such as time consumption and susceptibility to human error. The review sets a foundation by emphasizing the transition from conventional ML models, which require manual feature extraction, to DL frameworks that automate feature extraction and classification tasks.
2. Deep Learning Techniques: The paper is structured to explore various DL architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders (AEs), and other hybrid models. The discussion further categorizes CNNs into 1D and 2D approaches, illustrating their adaptability to process time-series data from EEG signals efficiently. The strength of CNNs lies in their capability to learn spatial hierarchies in data, making them suitable for epilepsy detection.
3. Datasets and Preprocessing: An integral part of this review is the acknowledgment of publicly available EEG datasets such as the CHB-MIT, Bonn, and others. The importance of robust preprocessing techniques for noise reduction and signal normalization, which are foundational for training effective DL models, is underscored.
4. Challenges and Constraints: Despite DL's potential, the paper identifies several challenges, including the scarcity of comprehensive labeled datasets, the variability in EEG data characteristics, and the computational resources needed for model training. The complex real-time application requirement poses another hurdle for deploying these models in clinical settings.
5. Applications Beyond EEG: Although the primary focus is on EEG-based methodologies, the paper also explores DL applications in MRI, sMRI, fMRI, and PET imaging for epilepsy detection. These modalities are generally leveraged for localizing epileptic foci rather than direct seizure detection due to their inherent operational constraints.
6. Hardware and Software Implementations: The paper touches on how DL-powered systems and hardware advancements can be utilized in practical, wearable seizure detection devices. The benefit of cloud-based infrastructures to support real-time analysis and prediction is argued.
7. Discussion and Future Directions: The final sections offer a reflection on the current state of DL in epilepsy detection, recognizing the promising results yet highlighting the continued need for improvement in model generalization, the integration of multimodal approaches, and real-world application feasibility.

Implications and Future Directions

The paper predicts substantial advancements in wearable technologies and cloud-based systems capable of real-time analysis, which could transform seizure management in clinical and personal settings. The potential for DL to predict interictal periods offers a compelling area for further research, promising to intervene proactively before the onset of seizures.

Future research could focus on improving the generalization capabilities of DL models, larger and more diverse datasets, and possibly integrating DL with other AI paradigms such as reinforcement learning to enhance decision-making processes in seizure prediction and management. The integration of DL with clinical workflows and patient-specific models holds potential for personalized medicine approaches in epilepsy treatment.

This review thoroughly encapsulates the landscape of epilepsy detection using DL, serving as a pivotal resource for researchers aiming to navigate the complexities and innovate within this space.