The use of Spindle Feature Vectors in Wearable Devices for Sleep Monitoring and Analysis

Abstract

The influence of sleep quality on humans health is considered as one of the most important aspects for preventative care. During the last decade, several wearable sensors have been developed for monitoring bio-signals. In this work, we present the applicability of an automatic software tool, called Spindle Detection and Feature Extraction (SpiDeFex), developed for the analysis of multi-channel signals of professional EEG systems, in the consumer area. Using commercial devices based on lightweight, rechargeable pods that can sense, collect and transmit an EEG signal in real-time, we can extract information for sleep quality for commercial applications. This work presents the architecture and functionality of SpiDeFex, and based on experimental results, we demonstrate how it can be used for sleep quality monitoring and analysis in a consumer wearable device.