Measurement, acquisition and processing of biopotential signals for early detection of sleep paralysis

Abstract

This paper presents a conceptual design of a wearable system for detecting and interrupting sleep paralysis in order to help patients with this disorder. The proposed solution, envisioned as a smart headband, integrates an accelerometer, electrooculography (EOG), and electroencephalography (EEG) for precise monitoring of the user's condition. The detection methodology is based on a hierarchical logic: first, the absence of head movement is analyzed, then the REM phase is identified through the analysis of the EOG signal, while the final confirmation is performed by analyzing the spectral analysis of the EEG signal (FFT and PSD). In a practical implementation, the system is expected to store recorded data and detected events for later analysis.