Feasibility of In-Ear Single-Channel ExG for Wearable Sleep Monitoring in Real-World Settings

• URL: http://arxiv.org/abs/2509.07896v2
• Date: Mon, 15 Sep 2025 08:34:10 GMT
• Title: Feasibility of In-Ear Single-Channel ExG for Wearable Sleep Monitoring in Real-World Settings
• Authors: Philipp Lepold, Jonas Leichtle, Tobias Röddiger, Michael Beigl,
• Abstract summary: We investigated the feasibility of using single-channel in-ear electrophysiological (ExG) signals for automatic sleep staging in a wearable device.<n>Our system achieved 90.5% accuracy for binary sleep detection (Awake vs. Asleep) and 65.1% accuracy for four-class staging (Awake, REM, Core, Deep) using leave-one-subject-out validation.
• Score: 5.212315750411577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG and full-scalp EEG for various phenomena, suggesting wearable, in-ear devices could allow unobtrusive sleep monitoring. We investigated the feasibility of using single-channel in-ear electrophysiological (ExG) signals for automatic sleep staging in a wearable device by conducting a sleep study with 11 participants (mean age: 24), using a custom earpiece with a dry eartip electrode (D\"atwyler SoftPulse) as a measurement electrode in one ear and a reference in the other. Ground truth sleep stages were obtained from an Apple Watch Ultra, validated for sleep staging. Our system achieved 90.5% accuracy for binary sleep detection (Awake vs. Asleep) and 65.1% accuracy for four-class staging (Awake, REM, Core, Deep) using leave-one-subject-out validation. These findings demonstrate the potential of in-ear electrodes as a low-effort, comfortable approach to sleep monitoring, with applications such as stopping podcasts when users fall asleep.

Related papers

• What Radio Waves Tell Us about Sleep [34.690382091650314]
  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.
  arXiv  Detail & Related papers  (2024-05-20T02:41:21Z)
• 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)
• SleepMore: Sleep Prediction at Scale via Multi-Device WiFi Sensing [0.0]
  We propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity. We validate SleepMore using data from a month-long user study involving 46 college students and draw comparisons with the Oura Ring wearable. Our results demonstrate that SleepMore produces statistically indistinguishable sleep statistics from the Oura ring baseline for predictions made within a 5% uncertainty rate.
  arXiv  Detail & Related papers  (2022-10-24T16:42:56Z)
• Development of Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels [0.0]
  We develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. A deep learning -based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86%) in all bipolar derivations available from the 4-electrode recordings.
  arXiv  Detail & Related papers  (2022-08-25T08:36:44Z)
• Using Ballistocardiography for Sleep Stage Classification [2.360019611990601]
  Current methods of sleep stage detection are expensive, invasive to a person's sleep, and not practical in a modern home setting. Ballistocardiography (BCG) is a non-invasive sensing technology that collects information by measuring the ballistic forces generated by the heart. We propose to implement a sleep stage detection algorithm and compare it against sleep stages extracted from a Fitbit Sense Smart Watch.
  arXiv  Detail & Related papers  (2022-02-02T14:02:48Z)
• In-Bed Person Monitoring Using Thermal Infrared Sensors [53.561797148529664]
  We use 'Griddy', a prototype with a Panasonic Grid-EYE, a low-resolution infrared thermopile array sensor, which offers more privacy. For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations. We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN)
  arXiv  Detail & Related papers  (2021-07-16T15:59:07Z)
• 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)
• WiSleep: Scalable Sleep Monitoring and Analytics Using Passive WiFi Sensing [0.0]
  WiSleep is a sleep monitoring and analytics platform using smartphone network connections that are passively sensed from WiFi infrastructure. We propose an unsupervised ensemble model of Bayesian change point detection to predict sleep and wake-up times. We show that WiSleep can process data from 20,000 users on a single commodity server, allowing it to scale to large campus populations with low server requirements.
  arXiv  Detail & Related papers  (2021-02-07T00:05:14Z)
• 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)
• 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.