Classification Of Sleep-Wake State In A Ballistocardiogram System Based On Deep Learning

• URL: http://arxiv.org/abs/2011.08977v1
• Date: Wed, 11 Nov 2020 03:38:33 GMT
• Title: Classification Of Sleep-Wake State In A Ballistocardiogram System Based On Deep Learning
• Authors: Nemath Ahmed, Aashit Singh, Srivyshnav KS, Gulshan Kumar, Gaurav Parchani, Vibhor Saran
• Abstract summary: 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.
• Score: 1.4680035572775534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment or conditions of the subject during their sleep. Techniques such as Polysomnography(PSG) are obtrusive and are not convenient for regular sleep monitoring. Fortunately, The rise of novel technologies and advanced computing has given a recent resurgence to monitoring sleep techniques. One such contactless and unobtrusive monitoring technique is Ballistocradiography(BCG), in which vitals are monitored by measuring the body's reaction to the cardiac ejection of blood. In this study, we propose a Multi-Head 1D-Convolution based Deep Neural Network to classify sleep-wake state and predict sleep-wake time accurately using the signals coming from a BCG sensor. Our method achieves a sleep-wake classification score of 95.5%, which is on par with researches based on the PSG system. We further conducted two independent studies in a controlled and uncontrolled environment to test the sleep-wake prediction accuracy. We achieve a score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature of the proposed system make it a convenient method for long term monitoring of sleep states.

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 Model -- A Sequence Model for Predicting the Next Sleep Stage [18.059360820527687]
  Sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG) or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy.
  arXiv  Detail & Related papers  (2023-02-17T07:37:54Z)
• 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)
• Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling [51.316408685035526]
  Learning new tasks and skills in succession without losing prior learning is a computational challenge for both artificial and biological neural networks. Here, we investigate how modeling three distinct components of mammalian sleep together affects continual learning in artificial neural networks.
  arXiv  Detail & Related papers  (2022-09-09T13:45:27Z)
• 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)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep [19.49661647406365]
  Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior. The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use. Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring.
  arXiv  Detail & Related papers  (2021-04-27T04:12:43Z)
• 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)
• Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals [0.0]
  We develop a network architecture with the aim of classifying the sleep stage in awake or asleep using only HR signals from a pulse oximeter. Transformers are able to model the sequence, learning the transition rules between sleep stages. The overall accuracy, specificity, sensibility, and Cohen's Kappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.
  arXiv  Detail & Related papers  (2021-01-29T22:58:33Z)
• 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.