Benchmarking performance of an automatic polysomnography scoring system in a population with suspected sleep disorders

Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques

Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disorders early is essential for effective treatment, which traditionally relies on polysomnogram (PSG) tests. However, diagnosing sleep disorders with high accuracy based solely on electroencephalogram (EEG) signals, rather than using various signals in a complex PSG, can reduce the time and cost required, and the need for specialized signal devices, as well as increase accessibility and usability. Previous studies have focused on traditional machine learning (ML) methods such as K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), and ensemble learning methods for sleep disorders analysis. However, these models require manual methods for feature extraction, and the prediction accuracy greatly depends on the type of feature extracted. Additionally, the EEG signal datasets are small and heterogeneous, challenging traditional machine learning and deep learning models. The study proposes an innovative multi-task learning convolutional neural network with a partially shared structure that uses frequency-time images generated from EEG signals to address these limitations. The proposed technique makes two predictions using non-shared features from time-frequency images created through Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT), one prediction from shared features, and the final prediction is a combination of these three predictions. The weights for this combination were optimized using the genetic algorithm and the Q-learning algorithm, aiming to minimize loss and maximize accuracy. The study utilizes a dataset involving 26 participants to examine the impact of Partial Sleep Deprivation (PSD) on EEG recordings. The outcomes demonstrated that the multi-task learning model using these two optimization methods, attained 98% accuracy on the test data for predicting partial sleep deprivation. This automated diagnostic model is an efficient supporting tool for rapidly and effectively diagnosing sleep disorders. It swiftly and precisely evaluates sleep data, minimizing the time and effort required by the patient and the physician.

Artificial intelligence in the diagnosis of obstructive sleep apnea: a scoping review

PURPOSE

The gold standard diagnostic modality of Obstructive Sleep Apnea (OSA) is polysomnography (PSG), which is resource-intensive, requires specialized facilities, and may not be accessible to all patients. There is a growing body of research exploring the potential of artificial intelligence (AI) to offer more accessible, efficient, and cost-effective alternatives for the diagnosis of OSA.

METHODS

We conducted a scoping review of studies applying AI techniques to diagnose and assess OSA in adult populations. A comprehensive search was performed in the Web of Science database using terms related to "obstructive sleep apnea," "artificial intelligence," "machine learning," and related approaches.

RESULTS

A total of 344 articles met the inclusion criteria. The findings highlight various methodologies of disease evaluation, including binary classification distinguishing between OSA-positive and OSA-negative individuals in 118 articles, OSA event detection in 211 articles, severity evaluation in 38 articles, topographic diagnostic evaluation in 8 articles, and apnea-hypopnea index (AHI) estimation in 26 articles. 40 distinct types of data sources were identified. The three most prevalent data types were electrocardiography (ECG), used in 108 articles, photoplethysmography (PPG) in 62 articles, and respiratory effort and body movement in 44 articles. The AI techniques most frequently applied were convolutional neural networks (CNNs) in 104 articles, support vector machines (SVMs) in 91 articles, and K-Nearest Neighbors (KNN) in 57 articles. Of these studies, 229 used direct patient recruitment, and 115 utilized existing datasets.

CONCLUSION

While AI demonstrates substantial potential with high accuracy rates in certain studies, challenges remain such as model transparency, validation across diverse populations, and seamless integration into clinical practice. These challenges may stem from factors such as overfitting to specific datasets, limited generalizability, and the need for standardized protocols in clinical settings.

Are scoring respiratory effort-related arousals worth the effort? --A study comparing outcomes between 4 % vs 3 % hypopnea scoring rules

STUDY OBJECTIVES

The respiratory effort-related arousal (RERA) has been combined with apneas and hypopneas into the respiratory disturbance index (RDI). RERAs are characterized by ≥ 10 s of increasing upper airway effort terminating in arousal without meeting hypopnea criteria. The recent change to hypopnea definitions now includes a ≥30 % reduction in airflow for 10 s with EITHER a 3 % oxygen desaturation OR an arousal. Consequently, many events previously categorized as RERAs will now be included in the 3 % hypopneas, likely reducing the number of events scored as RERAs. We hypothesized that the 3 % apnea-hypopnea index (3%AHI) would approximate the 4%RDI, with the number of 3 % RERAs being negligible.

RESEARCH QUESTION

How does the transition from the 4 % to the 3 % hypopnea rules impact the significance of RERAs in clinical practice, and how we should relate the AHI and RDI using the different hypopnea rules?

METHODS

We prospectively collected 76 consecutive polysomnography results in 4 adult age groups. We re-scored the respiratory events utilizing both the 3 % and the 4 % hypopnea rules and compared the outcomes.

RESULTS

Among 76 diagnostic studies (mean age 47.5 years, males 47.4 %), the 3 % RERA index [0.8 (0.0, 3.1)] [median (Q1, Q3)] was significantly lower than the 4 % RERA index [3.5 (1.0, 7.3)]. The 3%AHI was 3.07 ± 9.23 (mean ± SD) higher than the 4%RDI (p = 0.005). The 3%AHI was 8.63 ± 8.86 higher than the 4%AHI in all age groups (p < 0.001). This was mainly due to an increased hypopnea index (+8.51 ± 9.03, p < 0.001). In patients with obstructive sleep apnea (OSA), the 3%RERA contributes 4.3 % to the 3%RDI, while the 4%RERA contributes 27.7 % to the 4%RDI.

INTERPRETATIONS

Both 3%RDI and 3%AHI are higher than the 4%RDI, primarily due to identification of more hypopnea events, resulting in more patients being classified as having OSA. This change in criteria complicates the comparison of hypopnea and RERA contributions between sleep studies scored using the different hypopnea rules.

Effects of early midlife ovarian removal on sleep: Polysomnography-measured cortical arousal, homeostatic drive, and spindle characteristics

Bilateral salpingo-oophorectomy (BSO; removal of ovaries and fallopian tubes) prior to age 48 is associated with elevated risk for both Alzheimer's disease (AD) and sleep disorders such as insomnia and sleep apnea. In early midlife, individuals with BSO show reduced hippocampal volume, function, and hippocampal-dependent verbal episodic memory performance associated with changes in sleep. It is unknown whether BSO affects fine-grained sleep measurements (sleep microarchitecture) and how these changes might relate to hippocampal-dependent memory. We recruited thirty-six early midlife participants with BSO. Seventeen of these participants were taking 17β-estradiol therapy (BSO+ET) and 19 had never taken ET (BSO). Twenty age-matched control participants with intact ovaries (AMC) were also included. Overnight at-home polysomnography recordings were collected, along with subjective sleep quality and hot flash frequency. Multivariate Partial Least Squares (PLS) analysis was used to assess how sleep varied between groups. Compared to AMC, BSO without ET was associated with significantly decreased time spent in non-rapid eye movement (NREM) stage 2 sleep as well as increased NREM stage 2 and 3 beta power, NREM stage 2 delta power, and spindle power and maximum amplitude. Increased spindle maximum amplitude was negatively correlated with verbal episodic memory performance. Decreased sleep latency, increased sleep efficiency, and increased time spent in rapid eye movement sleep were observed for BSO+ET. Findings suggest there is an association between ovarian hormone loss and sleep microarchitecture, which may contribute to poorer cognitive outcomes and be ameliorated by ET.

An optimized framework for processing multicentric polysomnographic data incorporating expert human oversight

Introduction

Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers.

Methods

A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow.

Results

We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ.

Discussion

We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.

Neurobehavioral functions during recurrent periods of sleep restriction: effects of intra-individual variability in sleep duration

STUDY OBJECTIVES

To investigate whether neurobehavioral impairments are exacerbated during successive cycles of sleep restriction and recovery in young adults, and whether a variable short sleep schedule can mitigate these impairments relative to a stable one.

METHODS

Fifty-two healthy young adults (25 males, aged: 21-28) were randomly assigned to the stable short sleep group, the variable short sleep group, or the control group in this laboratory-based study. They underwent two baseline nights of 8-hour time-in-bed (TIB), followed by two cycles of "weekday" sleep opportunity manipulation and "weekend" recovery (8-hour TIB). During each manipulation period, the stable short sleep and the control groups received 6- and 8-hour TIBs each night respectively, while the variable short sleep group received 8-hour, 4-hour, 8-hour, 4-hour, and 6-hour TIBs from the first to the fifth night. Neurobehavioral functions were assessed five times each day.

RESULTS

The stable short sleep group showed faster vigilance deterioration in the second week of sleep restriction as compared to the first. This effect was not observed in the variable short sleep group. Subjective alertness and practice-based improvement in processing speed were attenuated in both short sleep groups.

CONCLUSIONS

In young adults, more variable short sleep schedules incorporating days of prophylactic or recovery sleep might mitigate compounding vigilance deficits resulting from recurrent cycles of sleep restriction. However, processing speed and subjective sleepiness were still impaired in both short sleep schedules. Getting sufficient sleep consistently is the only way to ensure optimal neurobehavioral functioning.

CLINICAL TRIAL

Performance, Mood, and Brain and Metabolic Functions During Different Sleep Schedules (STAVAR), https://www.clinicaltrials.gov/study/NCT04731662, NCT04731662.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices

AIMS

Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker).

FOCUS TECHNOLOGY

Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3.

REFERENCE TECHNOLOGY

In-lab polysomnography with 3-reader, consensus sleep scoring.

SAMPLE

Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years).

DESIGN

Overnight in a sleep laboratory from habitual sleep time to wake time.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography).

CORE OUTCOMES

EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33).

IMPORTANT ADDITIONAL OUTCOMES

Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset).

CORE CONCLUSION

EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Validation of a Wearable Medical Device for Automatic Diagnosis of OSA against Standard PSG

STUDY OBJECTIVE

The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis.

MATERIAL AND METHODS

This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022.

RESULTS

A total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07.

CONCLUSIONS

The AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.

Insights into vascular physiology from sleep photoplethysmography

STUDY OBJECTIVES

Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP).

METHODS

Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated.

RESULTS

BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency.

CONCLUSIONS

The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

Creating an Optimal Approach for Diagnosing Sleep Apnea

Sleep apnea is nowadays recognized as a treatable chronic disease and awareness of it has increased, leading to an upsurge in demand for diagnostic testing. Conventionally, diagnosis depends on overnight polysomnography in a sleep clinic, which is highly human-resource intensive and ignores the night-to-night variability in classical sleep apnea markers, such as the apnea-hypopnea index. In this review, the authors summarize the main improvements that could be made in the sleep apnea diagnosis strategy; how technological innovations and multi-night home testing could be used to simplify, increase access, and reduce costs of diagnostic testing while avoiding misclassification of severity.