Evaluation of the Diagnostic Capacity of a Smart Mattress Versus Conventional Polysomnography

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Obstructive Sleep Apnea (OSA)
• Intervention / Treatment: Device: Integrated polysomnographic assessment with smart mattress.

Detailed Description

This project corresponds to a descriptive, observational, and prospective study whose objective is to validate the functioning, accuracy, and clinical applicability of an intelligent mattress designed for the non-invasive detection of sleep-related respiratory disturbances, in comparison with level I polysomnography (the gold standard for OSA diagnosis). This phase constitutes the first stage of the global project aimed at the diagnosis and treatment of obstructive sleep apnea (OSA) through advanced monitoring and postural-adaptation technologies.

During the period between November 17, 2026, and March 1, 2028, all polysomnographies performed in the Sleep Unit of Hospital San Pedro will be incorporated into a systematic registry together with the data simultaneously generated by the intelligent mattress. The aim is to determine the level of agreement between both systems, validate the diagnostic utility of the mattress, and generate the database required for the subsequent development of artificial intelligence algorithms for the automatic identification of respiratory events and sleep stages.

1. REGISTRY PROCEDURES AND DATA QUALITY

   The centralized registry will include all polysomnographic studies performed with Natus equipment, stored on its internal server, and the parallel recordings from the intelligent mattress. The registry structure will be designed to ensure data traceability, integrity, and quality, with specific procedures for technical validation, clinical verification, and coherence control.

   1.1 Quality Assurance Plan

   A quality assurance system will be established based on four pillars:

   1. Initial technical validation

      Each polysomnography will be reviewed by a sleep technician who will confirm:

      • Correct sensor placement.
      • Absence of recording failures.
      • Presence of at least 4 valid hours of sleep.
      • Adequate synchronization with the mattress.

   2. Expert clinical review

      A certified sleep-medicine specialist will manually score each PSG according to AASM 2022 recommendations, including:

      • Classification of apneas and hypopneas.
      • Detection of micro-arousals.
      • Analysis of sleep architecture.
      • Quantification of supine and non-supine time.
      • Determination of global AHI, supine AHI, and non-supine AHI.

   3. Internal audit

      Ten percent of the records will be reviewed by a second independent evaluator to estimate inter-observer agreement. Discrepancies >10% in respiratory indices will trigger consensus sessions.

   4. Mattress data integrity review

   The system will automatically verify:

   • Temporal continuity of the recording.
   • Presence of stable BCG sensor signal.
   • Absence of failures in air chambers.
   • Integrity of the exported file.

   1.2 Automated Data Checks

   Mattress and PSG data will be subjected to validation through automated rules:

   • Range rules:

     • Oxygen saturation between 50-100%.
     • Respiratory rate between 6-40 rpm.
     • Heart rate between 35-180 bpm.
     • Total sleep time between 2-12 h.

   • Internal coherence rules:

     • Percentages of N1, N2, N3, and REM must sum ≤100%.
     • Number of events must be compatible with calculated AHI.
     • Time in bed must match total recording time.

   • Temporal consistency rules:

     • Synchronization between PSG and mattress ±5 seconds.

   Records generating alerts will be manually reviewed and classified as:

   • Valid.
   • Valid with warnings.
   • Unusable.

   1.3 Source Data Verification (SDV)

   A source-data verification process will be implemented, including:

   • Comparison of respiratory events detected by the mattress with those manually annotated in PSG.
   • Cross-checking of position (supine/non-supine) between both systems.
   • Verification of demographic variables against the clinical record.
   • Concordance of sleep time and arousals.

2. REGISTRY DATA DICTIONARY

   The study will include a comprehensive data dictionary specifying:

   • Variable name.
   • Operational definition.
   • Unit of measurement.
   • Method of acquisition.
   • Source (PSG, mattress, or derived)
   • Standard coding (AASM 2022, MedDRA for events)
   • Expected physiological ranges.

   Examples:

   • Global AHI: total number of apneas + hypopneas divided by sleep time in hours.
   • Micro-arousals: defined according to AASM as EEG frequency shifts ≥3 seconds.

3. STANDARD OPERATING PROCEDURES (SOPs)

   The study includes documented SOPs for:

   Installation, recording, and disconnection of PSG and mattress.

   Manual scoring of respiratory and leg events.

   Data export, anonymization, and archiving.

   Synchronization and technical verification.

   Data inconsistency management and queries.

   Quality control and internal auditing.

   Classification of incomplete records.

   Security, privacy, and GDPR compliance.

   Each SOP specifies responsibilities, operational steps, acceptance criteria, and incident-handling mechanisms.

4. SAMPLE SIZE ASSESSMENT

   The planned sample size is 500 complete records. This calculation is based on:

   - An estimated accessible population of ~700 PSGs in the reference period.

   - A 95% confidence level.

   • A 1% margin of error.
   • The need for high statistical power for concordance comparisons.
   • Requirements for training AI models with a significant volume of observations.

5. MISSING DATA MANAGEMENT PLAN

   Missing data will be classified as:

   • Technical missing: signal loss, sensor failures.

   • Poor-quality missing: valid time <4h.

   • Inconsistency missing: impossible ranges or temporal discrepancies.

   • Administrative missing: export errors.

   Criteria:

   - Exclusion of records with valid time <4h.

   • Simple imputation for position data when outcomes are unaffected.
   • Multiple imputation only for AI model training.
   • Documentation of missing-data reasons in the incident log.

6. STATISTICAL ANALYSIS PLAN

   The analysis will include:

   • Descriptive statistics: means, standard deviations, medians, ranges, and frequencies.
   • Correlation:

   Pearson or Spearman coefficients between PSG and mattress.

   • Agreement:

   Bland-Altman analyses for:

   - Global AHI.

   - Supine/non-supine AHI.

   - Periodic limb movements.

   -Sleep efficiency.

   • Diagnostic analysis:

     - Sensitivity.

     - Specificity.

     - PPV.

     - NPV.

     - ROC curves and AUC.

   • Predictive models:

     - Logistic regression.

     • CHAID trees.
     • Multivariate classification.
   • Multiple-comparison adjustment:

   Benjamini-Hochberg (FDR).

   Analysis will be conducted using SPSS and R. The significance level will be α = 0.05.

   This collection of procedures ensures the registry meets the necessary requirements for scientific validity, reproducibility, and the future development of automated AI-based models, while maintaining applicable clinical and regulatory standards.

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

• Study Contact: Name: Alejandra Roncero Lázaro, MD; Phone Number: 81864 + 34 941 29 80 00; Email: aroncerol@riojasalud.es
• Study Contact Backup: Name: Jorge Lázaro Galán, MSc; Phone Number: 89864 + 34 941 29 80 00; Email: jlazaro@riojasalud.es

Study Locations

• Spain
  • La Rioja
    • Logroño, La Rioja, Spain, 26006
      • Center for Biomedical Research of La Rioja
      • Contact: Alejandra Roncero Lázaro, MD; Phone Number: 81864 +34941298000; Email: aroncerol@riojasalud.es
      • Contact: Jorge Lázaro Galán, MSc; Phone Number: 89864 +34941 29 80 00; Email: jlazaro@riojasalud.es
      • Principal Investigator: ALEJANDRA RONCERO LAZARO, MD
      • Sub-Investigator: CARLOS RUIZ MARTINEZ, MD
      • Sub-Investigator: JORGE LAZARO GALAN, MS
    • Logroño, La Rioja, Spain, 26006
      • San Pedro University Hospital
      • Contact: Alejandra Roncero Lázaro, MD; Phone Number: 81864 +34941298000; Email: aroncerol@riojasalud.es
      • Principal Investigator: ALEJANDRA RONCERO LAZARO, MD
      • Sub-Investigator: CARLOS RUIZ MARTINEZ, MD
      • Sub-Investigator: JORGE LAZARO GALAN, MS
      • Contact: Jorge Lázaro Galán, MSc; Phone Number: +34941 29 80 00; Email: jlazaro@riojasalud.es

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Diagnostic polysomnographies performed at the Sleep Unit of San Pedro Hospital from November 17, 2026 to March 1, 2028.

Description

Inclusion Criteria:

• 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).

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

People with suspected obstructive sleep apnea (OSA); Patients will undergo the PSG on a smart mattress, which will allow simultaneous recording of: Standard PSG data, considered the gold standard in sleep studies. Data generated by the smart mattress, including signals and metrics related to movement, breathing, and other physiological parameters detectable by the device. The data obtained from the mattress will be compared with the PSG results in order to: Validate the mattress's ability to detect respiratory patterns and events during sleep. Optimize and train the mattress's artificial intelligence system, improving its diagnostic accuracy in identifying respiratory events and other sleep disturbances.

Device: Integrated polysomnographic assessment with smart mattress.; During 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic accuracy of the smart mattress for detecting sleep apnea.	Measured by: Sensitivity Specificity Predictive values (PPV/NPV) Compared with polysomnography (PSG).	Night of simultaneous PSG and mattress recording (one night per participant).

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Total sleep time	Compare the measurements of total sleep time, or the time the patient is asleep (minutes), with those recorded by conventional polysomnography.	Night of simultaneous PSG and mattress recording (one night per participant).
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.	Night of simultaneous PSG and mattress recording (one night per participant).
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.	Night of simultaneous PSG and mattress recording (one night per participant).
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.	Night of simultaneous PSG and mattress recording (one night per participant).
Descriptive demographic data.	Description of age, sex, BMI, comorbidities, and other relevant characteristics of the study population.	Night of simultaneous PSG and mattress recording (one night per participant).
Sleep efficiency	Compare the measurements of sleep efficiency, defined as the amount of time the patient spends asleep relative to the time spent in bed (minutes), with those recorded by conventional polysomnography.	Night of simultaneous PSG and mattress recording
Sleep stage	Compare the categorization of the sleep stage in which the subject is (N1, N2, N3, or REM stage) as determined by the smart mattress with that determined by conventional polysomnography. This categorization is based on respiratory rate measurements.	Night of simultaneous PSG and mattress recording
detections of microarousals	Compare the detections of microarousals recorded by the smart mattress (measured in Hz) with those detected by conventional polysomnography.	Night of simultaneous PSG and mattress recording

Collaborators and Investigators

• Sponsor: Hospital San Pedro de Logroño
• Collaborators: Fundacion Rioja Salud
• Investigators: Principal Investigator: Alejandra Roncero Lázaro, MD, Hospital Universitario San Pedro de Logroño

Publications and helpful links

General Publications

• 11. Feihong Ding, Andrew Cotton-Clay et al. Polysomnographic validation of an under-mattress monitorin device in estimating sleep architecture and obstructive sleep apnea in adults. Sleep Med. Abril 2022. DOI: 10.1016/j.sleep.2022.04.010
• 10. Jong-Ho Byun 1, Keun Tae Kim 1, Hye-Jin Moon 2, Gholam K Motamedi 3, Yong Won Cho 4. The first night effect during polysomnography, and patients' estimates of sleep quality
• 9. Welltech Electronics.
• 8. Kobayashi M, Namba K, Tsuili S, et al. Validdity of sheet-type portable monitoring device for screening obstrucitive sleep apnea síndrome. Sleep breath 2013; 17: 589-95.
• 7. Tenhunen M, Elomaa E, sistonen H et al, Emfit movement sensor in evaluating nocturnal breathing. Respir Physiol neurobiolo 2013; 187: 183-9.
• 6. Anttalainen, U. Polo, O. Vahlberg, T et AL. Reimbursed drugs in Patients with sleep-disordered breathing: a static charge-sensitive bed study. Sleep medicine 2010 11, 49-55.
• 5. Polo O, Brissaud L, sales B et al. The validity of the static charge sensitive bed in detecting obstructive sleep apneas. Eur repir J 1988; 1:330.
• 4. Mediano O, González Mangado N, Montserrat JM, Alonso-Álvarez ML, Almendros I, Alonso-Fernández A, et al. International Consensus Document on Obstructive Sleep Apnea. Arch Bronconeumol. 2022 Jan;58(1):52-68. doi: 10.1016/j.arbres.2021.03.017.
• 3. Benjafield, A. V., Ayas, N. T., Eastwood, P. R., Heinzer, R., Ip, M. S., Morrell, M. J., & Malhotra, A. (2019). Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. The Lancet Respiratory Medicine, 7(8), 687-698.
• 2. Senaratna, C. V., Perret, J. L., Lodge, C. J., Lowe, A., Campbell, B. E., Matheson, M. C., & Dharmage, S. C. (2017). Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Medicine Reviews, 34, 70-81.
• 1. Duran, J., Esnaola, S., Rubio, R., & Iztueta, A. (2001). Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. American Journal of Respiratory and Critical Care Medicine, 163(3), 685-689.

Study record dates

Study Major Dates

• Study Start (Estimated): November 17, 2026
• Primary Completion (Estimated): March 1, 2028
• Study Completion (Estimated): March 1, 2028

Study Registration Dates

• First Submitted: February 10, 2026
• First Submitted That Met QC Criteria: February 19, 2026
• First Posted (Actual): February 25, 2026

Study Record Updates

• Last Update Posted (Actual): March 3, 2026
• Last Update Submitted That Met QC Criteria: February 27, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Obstructive Sleep Apnea (OSA); Smart mattress; Sleep diagnosis; Artificial intelligence in healthcare; Sleep treatment innovation
• Additional Relevant MeSH Terms: Nervous System Diseases; Respiratory Tract Diseases; Respiration Disorders; Sleep Wake Disorders; Apnea; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Apnea Syndromes; Sleep Apnea, Obstructive
• Other Study ID Numbers: CEImLar PS-27

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Participant data is anonymized when obtained at the source.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No