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.
Polysomnographies performed at San Pedro Hospital between November 17, 2026, and March 1, 2028.
Polysomnographies of patients under 16 years of age and polysomnographies performed at San Pedro Hospital on patients over 16 years of age (as separate study groups).
Poor technical quality of the polysomnography.
Patients with >50% central apneas or presence of Cheyne-Stokes respiration (CSResp).
Lack of polysomnography analysis and/or mattress data.
Diagnostic polysomnographies performed at the Sleep Unit of San Pedro Hospital from November 17, 2026 to March 1, 2028.
Other
Prospective
Integrated polysomnographic assessment with smart mattress.
DeviceDuring a single night of recording, the participant will sleep on a smart mattress equipped with sensors for the continuous monitoring of sleep parameters. The data obtained will subsequently be compared and validated against polysomnography (PSG) recordings, considered the gold-standard reference for the objective evaluation of sleep architecture and quality, as well as respiratory events.
Diagnostic accuracy of the smart mattress for detecting sleep apnea.
Measured by: Sensitivity Specificity Predictive values (PPV/NPV) Compared with polysomnography (PSG).
Total sleep time
Compare the measurements of total sleep time, or the time the patient is asleep (minutes), with those recorded by conventional polysomnography.
Diagnostic accuracy according to sleep position.
Compare whether the amount of time (minutes) the patient spends in the lateral, prone, or supine position matches the amount of time recorded by conventional polysomnography.
Diagnostic accuracy by age group.
Compare whether the results obtained in the main measurements are equally reliable in adult and pediatric populations, using the values obtained from conventional polysomnography as the reference.
Subjective sleep quality (GSQS-8).
Assessment of the patient's perceived sleep quality and comparison with the objective mattress data. Name of the scale: Groningen Sleep Quality Scale * Minimum score: 0 * Maximum score: 14 The higher the score, the worse the outcome.
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Hospital San Pedro de Logroño
Lead sponsor
Fundacion Rioja Salud
Collaborator
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