Age estimation from sleep studies using deep learning predicts life expectancy

Brink-Kjær, Andreas; Leary, Eileen; Sun, Haoqi; Westover, M. Brandon; Stone, Katie L.; Peppard, Paul E.; Lane, Nancy E.; Cawthon, Peggy M.; Redline, Susan; Jennum, Poul; Sørensen, Henrik Rokkjær; Mignot, Emmanuel

doi:10.1038/s41746-022-00630-9

Cited by 13 publications

(6 citation statements)

References 65 publications

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“…This is an order of magnitude above much of the published work on EEG-based brain age and in line with the previous studies with the highest sample sizes (Brink-Kjaer et al, 2022;Sun et al, 2019). Critically, these studies focused on clinical or research datasets which are much more expensive and time-consuming to collect.…”

Section: Biomarker Measurement In Real-world Conditions With Mobile Eegsupporting

confidence: 78%

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Banville

Jaoude

Wood

et al. 2023

Preprint

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EEG is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications including brain-computer interfaces, epilepsy monitoring and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using machine learning with a four-channel consumer EEG headset. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, focusing on the emerging brain age framework as an example of biomarker discovery. With cross-validated R2 scores between 0.3-0.5, prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.

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Section: Biomarker Measurement In Real-world Conditions With Mobile Eegsupporting

confidence: 78%

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Banville

Jaoude

Wood

et al. 2023

Preprint

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“…In the field of machine learning applications that utilize sleep EEG metrics, prior studies predominantly relied on features such as spectral power composition and more basic morphological properties of slow waves and spindles [52][53][54] . Our analyses applied advanced methods to perform feature engineering from oscillatory events, enabling both subtyping of oscillatory events and more complex temporal associations of events that may increase the specificity and interpretability of predicted outcomes.…”

Section: Discussionmentioning

confidence: 99%

On Monitoring Brain Health from the Depths of Sleep: Feature Engineering and Machine Learning Insights for Digital Biomarker Development

McConnell,

Liu,

Biswas

et al. 2024

Preprint

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Background: Single-channel sleep electroencephalography (EEG) is a promising technology for creating cost-effective and widely accessible digital biomarkers for monitoring brain health. Sleep, notable for its numerous connections to brain health, is of particular interest in this context. Indeed, several of the best studied and widely recognized risk factors for neurodegenerative disease are also connected to aspects of sleep physiology, including biological sex, hypertension, diabetes, obesity/metabolic dysregulation, and immune system dysfunction. In this study, we utilize the unique signal characteristics of slow wave sleep (SWS) oscillatory events as features in machine learning models to predict underlying biological processes that are highly relevant to brain health. Our objective is to establish a foundation for algorithms capable of effectively monitoring physiological processes in sleep that directly and indirectly inform brain health using single-channel sleep EEG as a functional metric of brain activity. Methods: Utilizing data from the Cleveland Family Study, we analyzed 726 overnight polysomnography recordings to extract features from slow waves and adjacent oscillatory events. Advanced signal processing and machine learning techniques, including random forest models, were employed to engineer features and predict health-related outcomes such as age, cerebrovascular risk factors, endocrine functions, immune system activity, and sleep apnea. Results: Our models demonstrated significant predictive capability for several outcomes, including age (R2 = 0.643, p < 0.001), sex classification (area under the receiver operator characteristic (AUROC) curve = 0.808), diabetes and hypertension diagnosis (AUROC = 0.832 and 0.755, respectively). Significant predictions were also modeled for metabolic/endocrine functions (including blood concentrations of IGF-1, leptin, ghrelin, adiponectin, and glucose), and immune markers (including IL-6, TNF-alpha, and CRP). In addition, this approach provided successful predictions in regression modeling of BMI and both regression and classification of sleep apnea. Discussion: This study demonstrates the potential of using features from oscillatory events in single-channel sleep EEG as digital biomarkers. These biomarkers can identify key health and demographic factors that both affect brain health and are indicative of core brain functions. By capturing the complex interactions of neural, metabolic, endocrine, and immune systems during sleep, our findings support the development of single-channel EEG as a practical tool for monitoring complex biological processes through metrics that originate in brain physiology. Future research should aim to refine these digital biomarkers for broader home-based applications that may utilize inexpensive "wearable" devices to provide a scalable and accessible tool for tracking brain health-related outcomes.

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“…For example, neural networks can meaningfully predict patient-relevant outcomes, such as daytime sleepiness [ 56 ]. Going beyond OSA itself, the data contained within a PSG and processed via neural networks can predict mortality, with much of the risk attributable to sleep fragmentation [ 57 ]. Though, it would seem from recent data that OSA-event-related arousals alone do not provide additional information regarding incident CVD [ 58 ].…”

Section: Applying Machine Learning and Artificial Intelligence To Obs...mentioning

confidence: 99%

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Cohen,

Kundel,

Robson

et al. 2024

JCM

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Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea–hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

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Age estimation from sleep studies using deep learning predicts life expectancy

Cited by 13 publications

References 65 publications

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

On Monitoring Brain Health from the Depths of Sleep: Feature Engineering and Machine Learning Insights for Digital Biomarker Development

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

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