Advancements in EEG analysis for enhanced diagnosis and treatment of epilepsy: A comprehensive platform approach: Avances en el análisis del EEG para mejorar el diagnóstico y el tratamiento de la epilepsia: un enfoque integral basado en una plataforma.
Research Output: Contribution to specialist publication Article
Open access
Abstract
The diagnosis and treatment of epilepsy, a complex neurological disorder affecting millions worldwide, has undergone significant advancements with the integration of cutting-edge technology and innovative methodologies.
Electroencephalography (EEG) stands as a cornerstone in this domain, offering invaluable insights into brain activity and facilitating the identification of epileptic patterns. This abstract delves into the multifaceted landscape of epilepsy management, focusing on the development and implementation of a Comprehensive Platform for Visualizing and Analyzing Electroencephalograms (EEG).
From a clinical perspective, EEG is indispensable in diagnosing neurological
disorders, particularly epilepsy. Specific patterns in EEG signals can indicate epilepticactivity and help determine its type and origin. Additionally, EEG is fundamental in sleep disorder research, offering valuable insights into different sleep phases and their possible disturbances. In patients in comatose or altered consciousness states, this technique provides crucial data regarding brain activity levels (Apicella, Isgrò, Pollastro, & Prevete, 2023).
In research, EEG is an essential tool for analyzing how the brain processes
information and responds to different stimuli. The study of brain waves during
various cognitive tasks and mental states provides key insights into understanding processes such as cognition, emotion, and consciousness.
Pre-diagnosis helps identify possible cognitive deficiencies associated with epilepsy, brain wave behaviors to detect epileptiform spikes, and determine the need for a more detailed and specific evaluation. This can help professionals design an appropriate intervention plan to address the individual's needs.
The proposed solution is a Comprehensive EEG Visualization and Analysis System, focused on improving classification capabilities in neurology and psychology through advanced technology for EEG analysis and visualization. By combining data analysis, processing infrastructure, and visualization tools, the proposed system addresses current limitations in EEG analysis and establishes an intuitive and robust platform for future research and advances in diagnosing and treating neurological conditions.
The objective is to integrate and apply an algorithm in the web application to process EEG signals and extract relevant features. These features will be used to classify brain activity in terms of adverse elements present in the signal. Specifically, the aim is to classify brain activity into five distinct categories: epileptiform spikes, periodic lateralized discharges, generalized periodic discharges, lateralized rhythmic delta activity, and generalized rhythmic delta activity.
Electroencephalography (EEG) stands as a cornerstone in this domain, offering invaluable insights into brain activity and facilitating the identification of epileptic patterns. This abstract delves into the multifaceted landscape of epilepsy management, focusing on the development and implementation of a Comprehensive Platform for Visualizing and Analyzing Electroencephalograms (EEG).
From a clinical perspective, EEG is indispensable in diagnosing neurological
disorders, particularly epilepsy. Specific patterns in EEG signals can indicate epilepticactivity and help determine its type and origin. Additionally, EEG is fundamental in sleep disorder research, offering valuable insights into different sleep phases and their possible disturbances. In patients in comatose or altered consciousness states, this technique provides crucial data regarding brain activity levels (Apicella, Isgrò, Pollastro, & Prevete, 2023).
In research, EEG is an essential tool for analyzing how the brain processes
information and responds to different stimuli. The study of brain waves during
various cognitive tasks and mental states provides key insights into understanding processes such as cognition, emotion, and consciousness.
Pre-diagnosis helps identify possible cognitive deficiencies associated with epilepsy, brain wave behaviors to detect epileptiform spikes, and determine the need for a more detailed and specific evaluation. This can help professionals design an appropriate intervention plan to address the individual's needs.
The proposed solution is a Comprehensive EEG Visualization and Analysis System, focused on improving classification capabilities in neurology and psychology through advanced technology for EEG analysis and visualization. By combining data analysis, processing infrastructure, and visualization tools, the proposed system addresses current limitations in EEG analysis and establishes an intuitive and robust platform for future research and advances in diagnosing and treating neurological conditions.
The objective is to integrate and apply an algorithm in the web application to process EEG signals and extract relevant features. These features will be used to classify brain activity in terms of adverse elements present in the signal. Specifically, the aim is to classify brain activity into five distinct categories: epileptiform spikes, periodic lateralized discharges, generalized periodic discharges, lateralized rhythmic delta activity, and generalized rhythmic delta activity.
Publication Information
Output type
Research Output: Contribution to specialist publication Article
Original language
EnglishJournal (Volume, Issue Number)
International Symposium on Digital Transformation Publication milestones
- Published - 19/12/2024
Publication status
Published - 19/12/2024