Mining Biosignal Data (MBD)
Biomedical signals such as EEG, EMG, and EOG offer useful details to study and monitor physiological and mental status of individuals. These signals embed information that can drive a variety of applications such as sleep quality monitoring and sleep disorder diagnosis, eye tracking, neuro marketing, and distracted driving, to name a few. In this project, we intend to develop an open source generic framework that provides a set of primitive operators for mining and analysis of the biomedical signals collected by our mobile in-ear sensor. As a focus application, we are exploring use of this framework for sleep disorder diagnosis. Toward this end, first we are introducing custom feature extraction, feature selection and data classification algorithms to identify relevant signatures of sleep stages to be able to classify sleep stages and characterize sleep cycles. Next, we use our framework to perform causality analysis toward identification and isolation of the impact of environmental factors (such as temperature, humidity, light, etc.) on sleep cycle. As the last step, we will use our framework to diagnose sleep disorders by detecting the disease signatures in isolation from environmental factors. This project is performed in collaboration with the Mobile Networked Sensing Laboratory at University of Colorado Denver.