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

Rapid electroencephalography (EEG) devices are portable, easy-to-use systems that provide automated EEG interpretation to guide clinical decisions at the bedside. The scope of this review outlines the principles of automatic EEG analysis methods and the impact of recent developments in artificial intelligence and machine learning on these techniques in the context of seizure detection. The first generation of rapid EEG devices, primarily developed for anesthesia management, were tested for seizure detection; although raw EEG traces and power density arrays from these devices were helpful in seizure monitoring, their proprietary algorithms were not reliable for this purpose. The next generation of rapid EEG devices were principally aimed at seizure detection. In several large retrospective research studies, deep learning algorithms have demonstrated equal or superior proficiency as compared with human experts. Recent guidelines from the US Food and Drug Administration describe several guiding principles for this class of algorithms to be incorporated into medical devices. However, the "black box" nature of proprietary algorithms remains a concern because it hinders understanding of the device's limitations and potential pitfalls. We advocate for transparent reporting of essential software features to foster a constructive interaction between clinical users and rapid EEG device developers. This transparency is crucial for the continuous improvement of these devices, their acceptance among users, and ultimately, better patient care.