Technologies for sleep monitoring at home: wearables and nearables

Biomedical Engineering Letters, Journal Year: 2023, Volume and Issue: 13(3), P. 313 - 327

Published: July 7, 2023

Language: Английский

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Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics

Respirology, Journal Year: 2025, Volume and Issue: unknown

Published: March 3, 2025

ABSTRACT Underpinned by rigorous clinical trial data, the use of existing home sleep apnoea testing is now commonly employed for disordered breathing diagnostics in most centres globally. This has been a welcome addition field given considerable burden disease, cost, and access limitations with in‐laboratory polysomnography testing. However, approaches predominantly aim to replicate elements conventional different forms focus on estimation apnoea‐hypopnoea index. New, simplified technology screening, detection/diagnosis, or monitoring expanded exponentially recent years. Emerging innovations go beyond simple single‐night replication varying numbers signals setting. These novel have potential provide important new insights overcome many transform disease diagnosis management improve outcomes patients. Accordingly, current review summarises evidence study people suspected sleep‐related disorders, discusses emerging technologies according three key categories: (1) wearables (e.g., body‐worn sensors including wrist finger sensors), (2) nearables bed‐embedded bedside (3) airables audio video recordings), outlines their disruptive role care.

Language: Английский

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Screening for obstructive sleep apnea hypopnea using sleep breathing sounds based on the PSG-audio dataset

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107472 - 107472

Published: Jan. 1, 2025

Language: Английский

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Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review

Healthcare, Journal Year: 2025, Volume and Issue: 13(2), P. 181 - 181

Published: Jan. 17, 2025

Background: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition associated with major healthcare burden. Current diagnostic tools, such as full-night polysomnography (PSG), pose limited accessibility to diagnosis due their elevated costs. Recent advances in Artificial Intelligence (AI), including Machine Learning (ML) and deep learning (DL) algorithms, offer novel potential tools for an accurate OSA screening diagnosis. This systematic review evaluates articles employing AI-powered models the last decade. Methods: A comprehensive electronic search was performed on PubMed/MEDLINE, Google Scholar, SCOPUS databases. The included studies were original written English, reporting use of ML algorithms diagnose predict suspected patients. June 2024. registered PROSPERO (Registration ID: CRD42024563059). Results: Sixty-five articles, involving data from 109,046 patients, met inclusion criteria. Due heterogeneity outcomes analyzed into six sections (anthropometric indexes, imaging, electrocardiographic signals, respiratory oximetry miscellaneous signals). AI demonstrated significant improvements detection, accuracy, sensitivity, specificity often exceeding traditional tools. In particular, anthropometric indexes most widely used, especially logistic regression-powered algorithms. Conclusions: application has great improve patient outcomes, increase early lessen load systems. However, rigorous validation standardization efforts must be made standardize datasets.

Language: Английский

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Detection of sleep apnea using smartphone-embedded inertial measurement unit

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 28, 2025

Abstract We previously demonstrated that sleep apnea (SA) can be detected using acceleration and gyroscope signals from smartwatches. This study investigated whether an inertial measurement unit (IMU) embedded in non-wristwatch devices, such as smartphones, also detect SA when worn during sleep. During polysomnography (PSG), subjects wore IMU-embedded GPS device (Amue Link ® ) and/or smartphones (Xperia or iPhone on their abdomen. Triaxial were recorded overnight. Data split into training test groups (2:1) for each device. An algorithm was developed the to extract respiratory movements (0.13–0.70 Hz) events, which validated groups. IMU-derived events showed breath-by-breath concordance with PSG apnea-hypopnea yielding F1 scores of 0.786, 0.821, 0.796, respectively. Regression model derived IMU correlated AHI ( r = 0.90, 0.93, 0.96), limits agreement -16.7 25.9, -17.4 22.5, − 18.4 20.5. Using cutoff values groups, moderate-to-severe (AHI ≥ 15) identified AUCs 0.95, 0.98, 0.94 0.89, 0.96, 0.92, IMUs including quantitatively

Language: Английский

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Artificial Intelligence in Respiratory Health: A Review of AI-Driven Analysis of Oral and Nasal Breathing Sounds for Pulmonary Assessment

Electronics, Journal Year: 2025, Volume and Issue: 14(10), P. 1994 - 1994

Published: May 14, 2025

Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need accessible and convenient diagnostic tools health assessment. While traditional lung sound auscultation been primary method evaluating function, emerging research highlights potential nasal oral breathing sounds. These sounds, shaped by upper airway, serve as valuable non-invasive biomarkers detection. Recent advancements in artificial intelligence (AI) have significantly enhanced analysis enabling automated feature extraction pattern recognition from spectral temporal characteristics or even raw acoustic signals. AI-driven models demonstrated promising accuracy detecting conditions, paving way real-time, smartphone-based monitoring. This review examines AI-enhanced analysis, discussing methodologies, available datasets, future directions toward scalable solutions.

Language: Английский

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Deep-learning based sleep apnea detection using sleep sound, SpO2, and pulse rate

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(8), P. 4869 - 4874

Published: May 14, 2024

Language: Английский

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In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

JAMA Otolaryngology–Head & Neck Surgery, Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 16, 2023

Importance Consumer-level sleep analysis technologies have the potential to revolutionize screening for obstructive apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing predictability using sound recorded from smartphones 2 PSG at home important. Objective To validate performance a model breathing in conjunction home. Design, Setting, and Participants This diagnostic study followed prospective design, involving participants who underwent unattended PSG. Breathing sounds were during smartphones, one an iOS operating system other Android system, simultaneously participants’ own environment. 19 years older, slept alone, had either been diagnosed or no previous diagnosis. The was between February 2022 2023. Main Outcomes Measures Sensitivity, specificity, positive predictive value, negative accuracy sounds. Results Of 101 included duration, mean (SD) age 48.3 (14.9) years, 51 (50.5%) female. For smartphone, sensitivity values apnea-hypopnea index (AHI) levels 5, 15, 30 per hour 92.6%, 90.9%, 93.3%, respectively, specificities 84.3%, 94.4%, respectively. Similarly, AHI 92.2%, 90.0%, 92.9%, 84.0%, 94.3%, smartphone 88.6%, 88.1%, 93.1%, 94.1% hour, Conclusions Relevance demonstrated feasibility predicting reasonable obtained by

Language: Английский

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Embedded IoT Data Collection for Snore Analysis

International Journal of Advanced Trends in Computer Science and Engineering, Journal Year: 2024, Volume and Issue: 13(3), P. 119 - 123

Published: June 8, 2024

Internet of Things (IoT)-based devices are in demand for capturing different data types to produce essential information the receiver. Human sleep behavior is one area open research, particularly on human snoring. The ESP32 microcontroller was used modify processes snoring during sleep. This embedded IoT-based device monitors and captures activity being while sleeping. A prototype modified with its new algorithm developed, test experiments were conducted system performance. Experiment results showcased accuracy frequencies beyond established norms measuring decibel levels within specific parameters. Technical challenges encountered, such as static interferences storage errors, but all systematically addressed, highlighting system's robustness. Pilot EXP1 EXP2 provided insights into adaptability environmental conditions. It recommended incorporate upgraded machine learning algorithms a more powerful improve noise differentiation computational capabilities collaborate experts enable diagnostic capabilities. research emphasizes potential real-world application advancing healthcare solutions need continuous evolution

Language: Английский

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SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7782 - 7782

Published: Dec. 5, 2024

Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard diagnosing SAS polysomnography (PSG), an intrusive procedure that depends on subjective assessment by expert clinicians. To address limitations PSG, we propose a decision support system, which uses tracheal microphone data collection and deep learning (DL) approach-namely SiCRNN-to detect events overnight sleep recordings. Our proposed SiCRNN processes Mel spectrograms using Siamese approach, integrating convolutional neural network (CNN) backbone bidirectional gated recurrent unit (GRU). final detection

Language: Английский

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