Epileptic seizure detection using Deep Learning
ABSTARCT :
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals.
The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data.
Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance.
As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features.
The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’.
The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.
EXISTING SYSTEM :
The existence and non-existence of seizures. They also attained very promising overall accuracy, ranging from 97.72 to 100% for different cases. The random forest (RF) classifier has also been implemented in some studies
In raw biological signals, noise and artifacts often exist due to muscle and eye movements.
Seizure Heterogeneity: one of the factors that hinder the performance of the ES detection model is the heterogeneity of seizures. Consequently, there exists an imperative need for developing a ML model that is robust to the heterogeneity of epileptic seizure.
DISADVANTAGE :
Insufficient Labeled Data: Deep learning models require large amounts of labeled data to train effectively. For epileptic seizure detection, obtaining high-quality, labeled data from patients can be difficult due to privacy concerns, rarity of seizures, or the need for specialized equipment to collect data.
Black-box Nature: Deep learning models are often considered "black boxes," meaning that it can be challenging to understand how decisions are made. This lack of interpretability can be problematic in medical applications, where it is important to trust and explain the model's predictions, especially in critical healthcare settings.
Overfitting: Deep learning models are highly susceptible to overfitting, especially when the dataset is not sufficiently diverse or large. If the model overfits to a small set of data, it may fail to generalize to new, unseen patients or seizure types, leading to poor performance in real-world settings.
False Alerts: A significant challenge is minimizing false positives (incorrectly identifying a seizure when there isn't one) and false negatives (failing to detect a seizure when one occurs). Both types of errors can have serious consequences, such as unnecessary medical intervention or missed opportunities for treatment.
PROPOSED SYSTEM :
The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals
Nevertheless, the proposed work suffered from the limitation of not being able to detect the onset of a seizure, as it was not within the scope of our research
Furthermore, our proposed method has the potential to reduce the heavy clinical workload of neurologists in the medical system and would enable early seizure diagnosis and treatment for patients.
ADVANTAGE :
Real-time Monitoring: Deep learning models can analyze EEG (electroencephalogram) and other sensor data in real time, providing immediate detection of seizures. This enables quicker response times, allowing for timely interventions and possibly preventing injury or complications.
Advanced Pattern Recognition: Deep learning excels at identifying complex patterns in large datasets. It can detect seizures with high accuracy by analyzing features in the EEG signals that are difficult for human clinicians to notice. This results in more precise detection compared to traditional rule-based systems.
Wearable Devices: With deep learning models integrated into wearable devices, patients can be continuously monitored without the need for frequent doctor visits or in-hospital stays. These devices can detect seizures anytime and anywhere, enhancing the quality of life for patients.
Transfer Learning: Pre-trained deep learning models can be adapted for specific patients or populations, which reduces the need for extensive retraining and makes it easier to deploy in diverse clinical settings.
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