The gold standard for sleep monitoring is defined in the AASM Manual for the Scoring of Sleep and Associated Events (American Academy of Sleep Medicine [AASM], 2023). In this manual the criteria for polysomnography (PSG) are described. This type of measurement is believed to be technically difficult and expensive (Pan et al, 2020). This creates a high barrier to testing (wearable) sleep monitoring systems proposed in the scientific literature. Wearable systems already available on the (consumer) market are often made with black box technology and are not accurate enough on an individual level (Kainec et al., 2024). 

These are gaps in the current knowledge. This project investigated whether an affordable, accessible way can be found to build an open-source PSG system that can measure sleep according to the principles of the AASM. The principles of PSG have been investigated. The necessary resources have been investigated. A prototype has been made. This prototype records raw data using Arduino, which opens up the hardware and software design. Test measurements have been made, sleep assessment of the measurements was done manually by an experienced technician. Quality assessment of the technically most difficult type of signal, electro encephalography (EEG) was done using siginal-to-noise ratio (SNR) calculation. This resulted in positive SNR values for all connected EEG channels. 

Although not completely consistent, the SNR values varied according to the expected amount of noise, based on physiological variations of the different hypnogram stages. The highest positive values were found during deep sleep, where normally the highest amplitudes are measured and no artifact contamination of electromyography and/ or electro-oculography is present. The lowest values were found during wake, where mean amplitudes are lower and electromyography as well as electro-oculography artifact is present. It seems justified to conclude that it is possible to measure sleep that meets the requirements of the AASM with an open-source wearable system, based on Arduino."