Rapid-EEG Software Architecture's Clinical Impact: Advantages and Limitations.

Area of Science:

• Neurology
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Rapid electroencephalography (EEG) devices offer portable, automated interpretation for bedside clinical decisions.
• Early devices, designed for anesthesia, showed limitations in reliable seizure detection due to proprietary algorithms.
• Advancements focus on seizure detection, leveraging AI and machine learning for improved accuracy.

Purpose of the Study:

• To review automatic EEG analysis methods and the impact of AI/ML on seizure detection.
• To assess the evolution and effectiveness of rapid EEG devices for epilepsy monitoring.
• To address concerns regarding the transparency of proprietary algorithms in medical devices.

Main Methods:

• Review of principles in automatic EEG analysis.
• Analysis of the impact of artificial intelligence and machine learning on EEG interpretation.
• Evaluation of deep learning algorithm performance in seizure detection studies.

Main Results:

• First-generation rapid EEG devices had unreliable proprietary algorithms for seizure detection.
• Deep learning algorithms in newer devices demonstrate performance comparable or superior to human experts.
• US Food and Drug Administration provides guidelines for incorporating AI algorithms into medical devices.

Conclusions:

• Rapid EEG devices are evolving, with AI significantly enhancing seizure detection capabilities.
• The 'black box' nature of proprietary algorithms presents challenges in understanding device limitations.
• Transparent reporting of software features is essential for device improvement, user acceptance, and patient care.