This project implements a non-invasive sleep monitoring system using a bed pressure sensor array. The system detects changes in the contact pressure between a subject and the bed and is able to automatically select the sensor with the best respiratory signal, determine the respiratory rate (RR), count number of sleep apneas and count body position changes through the night. The respiratory signal is validated with an airflow sensor using Pearson's correlation coefficient. To determine the performance of body position and apnea detection algorithms, the sensibility and positive predictivity is computed on preliminary data and known records from a Physionet database. Real data is obtained from 5 subjects totaling 39 hours measured at home during a full night sleep, in a non-invasive way. The data is used to calculate relevant parameters to estimate a sleep quality. Cumulative frequency of sleep interval duration is proposed as a novel metric for sleep assessment.