Epileptic Seizure Detection Using Machine Learning Techniques

2021 ◽  
pp. 187-200
Author(s):  
Can Eyupoglu
Keyword(s):  
Machine Learning ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Complex Nature ◽  
Medical Doctors ◽  
Brain Disorder ◽  
Learning Techniques ◽  
Eeg Recordings

Epilepsy is a brain disorder that can be defined as a short-time and temporary occurrence of symptoms because of abnormal extreme or synchronous neuronal activity of the brain. Almost one percent of the world's population is struggling with epilepsy illness. The detection of epileptic seizures is mainly realized with reading the electroencephalogram (EEG) recordings by medical doctors due to the unpredictable and complex nature of the disease. This process takes much time and depends on the expert's experience. For this reason, automatic seizure detection using EEG recordings is necessary and of great importance for the comfort of medical doctors and patients. While detecting epileptic seizure automatically, machine learning techniques are used in the field of computer science. This chapter deals with the methods, approaches, models, and techniques which are utilized to detect epileptic seizures.

scholarly journals Epileptic Seizure Detection With Permutation Fuzzy Entropy Using Robust Machine Learning Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 182238-182258 ◽  
Author(s):  
Waqar Hussain ◽  
Muhammad Shahid Iqbal ◽  
Jie Xiang ◽  
Bin Wang ◽  
Yan Niu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Fuzzy Entropy ◽  
Machine Learning Techniques ◽  
Epileptic Seizure Detection ◽  
Learning Techniques

A Survey on Epilepsy Seizure Detection Using Machine Learning Technique

2020 ◽  
pp. 109-114
Author(s):  
Saranya N ◽  
Karthika Renuka D
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Chronic Condition ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Normal Brain ◽  
Learning Techniques ◽  
Normal Brain Function ◽  
Artificial Intelligence Technology

Epilepsy, One of the most prevalent neurological disorder. Its a chronic condition is characterized by voluntary, unpredictable, and recurrent seizures that affects millions of individuals worldwide. A brief alteration in normal brain function that affects the health of patients occurs in this chronic condition. Detection of epileptic seizures before the start of the onset is beneficial. Recent studies have suggested approaches to machine learning that automatically execute those diagnostic tasks by integrating statistics and computer science. Machine learning, an application of AI (Artificial Intelligence) technology, allows a machine to learn something new automatically and thereby improve its output through meaningful data. For the prediction of epileptic seizures from electroencephalogram (EEG) signals, machine learning techniques and computational methods are used. There is a vast amount of medical data available today about the disease, its symptoms, causes of illness and its effects. But this data is not analyzed properly to predict or to study a disease. The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare. So that seizures can be predicted earlier before it occurs, it will be useful for epilepsy patients to improve their safety and quality of their life.

A Survey on Epilepsy Seizure Detection Using Machine Learning Technique

2020 ◽  
pp. 109-114
Author(s):  
Saranya N ◽  
Karthika Renuka D
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Chronic Condition ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Machine Learning Techniques ◽  
Normal Brain ◽  
Learning Techniques ◽  
Normal Brain Function ◽  
Artificial Intelligence Technology

Epilepsy, One of the most prevalent neurological disorder. Its a chronic condition is characterized by voluntary, unpredictable, and recurrent seizures that affects millions of individuals worldwide. A brief alteration in normal brain function that affects the health of patients occurs in this chronic condition. Detection of epileptic seizures before the start of the onset is beneficial. Recent studies have suggested approaches to machine learning that automatically execute those diagnostic tasks by integrating statistics and computer science. Machine learning, an application of AI (Artificial Intelligence) technology, allows a machine to learn something new automatically and thereby improve its output through meaningful data. For the prediction of epileptic seizures from electroencephalogram (EEG) signals, machine learning techniques and computational methods are used. There is a vast amount of medical data available today about the disease, its symptoms, causes of illness and its effects. But this data is not analyzed properly to predict or to study a disease. The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare. So that seizures can be predicted earlier before it occurs, it will be useful for epilepsy patients to improve their safety and quality of their life.

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

Sign in / Sign up

Export Citation Format

Share Document