Insomnia Identification via Electroencephalography

• URL: http://arxiv.org/abs/2402.06251v1
• Date: Fri, 9 Feb 2024 08:59:37 GMT
• Title: Insomnia Identification via Electroencephalography
• Authors: Olviya Udeshika, Dilshan Lakshitha, Nilantha Premakumara, Surangani Bandara
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Insomnia is a serious sleep disorder caused by abnormal or excessive neural activity in the brain. An estimated 50 million people worldwide are thought to be affected by this condition, which is the second most severe neurological disease after stroke. In order to ensure a quick recovery, an early and accurate diagnosis of insomnia enables more effective drug and treatment administration. This study proposes a method that uses deep learning to automatically identify patients with insomnia. A set of optimal features are extracted from spectral and temporal domains, including the relative power of {\sigma}, \b{eta} and {\gamma} bands, the total power, the absolute slow wave power, the power ratios of {\theta}, {\alpha}, {\gamma}, \b{eta}, {\theta}/{\alpha}, {\theta}/\b{eta}, {\alpha}/{\gamma} and {\alpha}/\b{eta}, mean, zero crossing rate, mobility, complexity, sleep efficiency and total sleep time, to accurately quantify the differences between insomnia patients and healthy subjects and develops a 1D CNN model for the classification process. With the experiments use Fp2 and C4 EEG channels with 50 insomnia patients and 50 healthy subjects, the proposed model arrives 99.34% accuracy without sleep stage annotation. Using the features only from a single channel, the study proposes a smart solution for insomnia patients which allows machine learning to be to simplify current sleep monitoring hardware and improve in-home ambulatory monitoring.

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 Stage Classification Using a Pre-trained Deep Learning Model [0.0]
  "EEGMobile" is a machine-learning model that learns from electroencephalogram (EEG) spectrograms of brain signals. The model achieved an accuracy of 86.97% on a publicly available dataset named "Sleep-EDF20", outperforming other models proposed by different researchers.
  arXiv  Detail & Related papers  (2023-09-12T23:02:19Z)
• 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)
• Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals [35.141080847207355]
  Sleep stage classification based on deep brain recording has great potential to provide more precise treatment for patients. We proposed an applicable cross-modal transfer learning method for sleep stage classification with implanted devices. We tested the model with deep brain recording data from 12 patients with Parkinson's disease.
  arXiv  Detail & Related papers  (2023-02-07T03:21:33Z)
• 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)
• Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation [27.681891555949672]
  This research aims to increase the accuracy and reduce the learning time of Convolutional Neural Network. The proposed system uses Leaky Rectified Linear Unit (ReLU) instead of sigmoid activation function as an activation function.
  arXiv  Detail & Related papers  (2022-02-18T18:29:15Z)
• 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)
• 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)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.