What Radio Waves Tell Us about Sleep

• URL: http://arxiv.org/abs/2405.11739v2
• Date: Sat, 20 Jul 2024 19:38:37 GMT
• Title: What Radio Waves Tell Us about Sleep
• Authors: Hao He, Chao Li, Wolfgang Ganglberger, Kaileigh Gallagher, Rumen Hristov, Michail Ouroutzoglou, Haoqi Sun, Jimeng Sun, Brandon Westover, Dina Katabi,
• Abstract summary: We develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. We show that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM) and detects sleep apnea (AUROC = 0.88) The model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders.
• Score: 34.690382091650314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Related papers

• Insomnia Identification via Electroencephalography [0.0]
  An estimated 50 million people worldwide are thought to be affected by insomnia. This study proposes a method that uses deep learning to automatically identify patients with insomnia.
  arXiv  Detail & Related papers  (2024-02-09T08:59:37Z)
• A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals [0.0]
  Photoplethys (mography) has been demonstrated to be an effective signal for sleep stage inference. In this work, we present a machine learning sleep-wake classification model based on the eXtreme Gradient Boosting algorithm. The performance of our method was comparable to current state-of-the-art methods with a Sensitivity of 91.15 $pm$ 1.16%, Specificity of 53.66 $pm$ 1.12%, F1-score of 83.88 $pm$ 0.56%, and Kappa of 48.0 $pm$ 0.86%.
  arXiv  Detail & Related papers  (2023-08-07T13:43:19Z)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing [4.268772592648502]
  Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. Traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We developed a rule-based Natural Language Processing (NLP) algorithm, machine learning models, and Large Language Model(LLM)-based NLP algorithms to automate the extraction of sleep-related concepts.
  arXiv  Detail & Related papers  (2022-03-08T21:20:19Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea [0.7101004731620997]
  Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder. It is difficult for a physician to decide whether to prescribe polysomnography. Data from a complete night can be analyzed in about one second on a smartphone.
  arXiv  Detail & Related papers  (2021-01-07T15:38:28Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Classification Of Sleep-Wake State In A Ballistocardiogram System Based On Deep Learning [1.4680035572775534]
  We propose a Multi-Head 1D-Convolution based Deep Neural Network to classify sleep-wake state and predict sleep-wake time accurately. Our method achieves a sleep-wake classification score of 95.5%, which is on par with researches based on the PSG system.
  arXiv  Detail & Related papers  (2020-11-11T03:38:33Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.