Washington University in St . Louis School of Engineering and Applied Science Electrical and Systems Engineering Department ESE 498

Sleep is a necessary part of human life. In today’s world, the amount of time we spend sleeping can be regularly lower than the National Institutes of Health recommendation of 7 – 8 hours. Studies have shown that waking up during Rapid Eye Movement (REM) or Stage 1 sleep results in low sleep inertia. Sleep inertia is a state of lowered arousal that occurs upon waking from sleep and results in temporarily reduced performance. Similarly, studies show that waking during slow wave sleep (SWS) causes the most sleep inertia out of all the sleep stages. In addition, sleep inertia becomes worse with sleep deprivation. The goal of our project is to create a device that improves lifestyle and productivity by targeting the sleep stage at waking as something that can be optimized. HypnoLarm is a pillow device that records and analyzes electroencephalogram (EEG) signals that characterize sleep stage. Using this information and user-input waking time range, HypnoLarm will be able to wake the user at the optimal time to minimize sleep inertia while maximizing sleep time. This semester we have been able to build analog circuitry to collect/filter 8 channels of EEG signal as well as develop algorithms to interpret the data. Acknowledgements The authors would like to thank Dr. Robert Morley for his guidance and expertise in design. Without his help we would not have made as much progress as we did. 4 Problem Formulation Problem Statement Sleep inertia is the phenomena of disorientation and reduced performance that occurs immediately after waking. The time duration of sleep inertia can last from one minute to four hours [1]. Literature has shown that waking during stage 1 or stage 2 sleep reduces the effects of sleep inertia. In this project, we aim to design a device that will optimize the waking stage of the user and his/her desired waking time. By use of electroencephalography (EEG), we aim to be more accurate in determining user sleep stage than current commercial products that enhance user sleep. The ultimate goal of our device is to reduce user sleep inertia in order to increase productivity. Ideal Project Specifications Sensor Sheet The sensor sheet is composed of a layered sheet with a memory foam base for comfort. This sheet is embedded with EEG electrodes and localization sensors. The electrodes are spaced 1 inch apart, centerto-center in order to best replicate clinical EEG setting. The localization sensors are placed in a layout such that their data output can be used to determine the orientation and position of the user’s rested head. The layered sheet is composed of materials of different density in order to maximize both comfort of the sheet and electrode contact with the user. Central Processing Circuit The central processing circuit is composed of a processing board and auxiliary circuit components that allow piping of data produced by the sensor sheet to and from the processing board. Prior to sending sensor sheet data to the Mobile App, the central processing circuit must multiplex, amplify and filter the sensor sheet data to get both meaningful EEG and localization signals. These signals are then stored in memory as a data time series until sufficient data to produce a sample is generated. The sample is then transferred over Bluetooth to the Mobile App for sleep analytics processing. After transfer, a percentage of the buffer is flushed in order to ensure some amount of time overlap between samples.