This project aims to develop and evaluate an innovative, non-invasive diagnostic system based on a smart mattress for detecting obstructive sleep apnea (OSA), as well as assessing overall sleep quality and identifying periodic limb movements. The main goal is to improve the accuracy of sleep apnea diagnosis while providing a less invasive solution suitable for home use, ultimately enhancing patients' quality of life.
A descriptive, observational, prospective study will be conducted to analyze data obtained from diagnostic polysomnographies performed at the Sleep Unit of San Pedro Hospital between November 17, 2026, and March 1, 2028. Patients will use the smart mattress, and its measurements will be compared with polysomnography results. This comparison will allow for the optimization of the mattress's artificial intelligence, training it to accurately recognize respiratory patterns and sleep-related events, including positional apneas and periodic limb movements.
Key technical objectives include:
Determining the sensitivity, specificity, and predictive values of the mattress in detecting apneas, hypopneas, and limb movements compared to polysomnography.
Evaluating the agreement between the mattress and polysomnography for sleep variables such as total sleep time, sleep efficiency, sleep stages, micro-arousals, and patient position.
Assessing whether measurement accuracy varies by sleeping position or age group (adults vs. children).
Measuring subjective sleep quality using the Groningen Sleep Quality Scale (GSQS-8).
Performing a descriptive analysis of patient demographics.
Hypotheses:
The smart mattress will detect obstructive sleep apnea, sleep quality, and periodic limb movements with accuracy comparable to polysomnography.
The system will provide a reliable, non-invasive, home-friendly diagnostic method.
Measurements of the apnea-hypopnea index (AHI) and limb movements will show high sensitivity, specificity, and predictive values, both overall and according to OSA severity.
There will be good agreement between mattress measurements and polysomnography for most sleep variables.
Accuracy may vary depending on the patient's sleeping position.
Measurements will correlate well across adults and pediatric patients.
Subjective sleep quality scores (GSQS-8) will be consistent with objective mattress data.
This project seeks to develop a more accurate, accessible, and non-invasive diagnostic system for OSA, combining advanced technology with ease of home use. By training the mattress's AI to recognize sleep patterns and events, it aims to optimize the detection of positional apneas, providing patients with better monitoring, early intervention, and improved quality of life.