Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning And Classifier Combination For Home-based Applications

• URL: http://arxiv.org/abs/2110.00660v1
• Date: Fri, 1 Oct 2021 21:39:23 GMT
• Title: Online Obstructive Sleep Apnea Detection Based on Hybrid Machine Learning And Classifier Combination For Home-based Applications
• Authors: Hosna Ghandeharioun
• Abstract summary: obstructive sleep apnea (OSA) is one of the most prevalent diseases of the current century. In this paper, several configurations for online OSA detection are proposed. The proposed method has advantages like limited use of biological signals, automatic detection, online working scheme, and uniform and acceptable performance (over 85%) in all the employed databases.
• Score: 3.199352681587309
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automatic detection of obstructive sleep apnea (OSA) is in great demand. OSA is one of the most prevalent diseases of the current century and established comorbidity to Covid-19. OSA is characterized by complete or relative breathing pauses during sleep. According to medical observations, if OSA remained unrecognized and un-treated, it may lead to physical and mental complications. The gold standard of scoring OSA severity is the time-consuming and expensive method of polysomnography (PSG). The idea of online home-based surveillance of OSA is welcome. It serves as an effective way for spurred detection and reference of patients to sleep clinics. In addition, it can perform automatic control of the therapeutic/assistive devices. In this paper, several configurations for online OSA detection are proposed. The best configuration uses both ECG and SpO2 signals for feature extraction and MI analysis for feature reduction. Various methods of supervised machine learning are exploited for classification. Finally, to reach the best result, the most successful classifiers in sensitivity and specificity are combined in groups of three members with four different combination methods. The proposed method has advantages like limited use of biological signals, automatic detection, online working scheme, and uniform and acceptable performance (over 85%) in all the employed databases. These advantages have not been integrated in previous published methods.

Related papers

• STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers [2.872498492478085]
  We develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) We apply STREAMLINE to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA) Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association.
  arXiv  Detail & Related papers  (2023-12-09T04:12:38Z)
• AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning [1.5381930379183162]
  OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG) We propose a convolutional deep learning architecture able to reduce the temporal resolution of raw waveform data.
  arXiv  Detail & Related papers  (2023-02-10T11:21:47Z)
• Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection [70.86672569101536]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. This paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function.
  arXiv  Detail & Related papers  (2022-10-29T09:18:41Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• 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)
• Neural networks for Anatomical Therapeutic Chemical (ATC) [83.73971067918333]
  We propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM) Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature.
  arXiv  Detail & Related papers  (2021-01-22T19:49:47Z)
• Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection [11.879849130630406]
  Presentation attack detection (PAD) methods are used to determine whether samples stem from a bona fide subject or from a presentation attack instrument (PAI) We propose a new PAD technique based on autoencoders (AEs) trained only on bona fide samples (i.e. one-class) captured in the short wave infrared domain.
  arXiv  Detail & Related papers  (2020-08-18T15:33:41Z)
• Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks [4.882119124419393]
  This paper presents a CNN architecture with 1D convolutional and FCN layers for classification. The proposed 1D CNN model achieves excellent classification results without manually preprocesssing PSG signals.
  arXiv  Detail & Related papers  (2020-06-13T15:35:18Z)
• 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)
• Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features [5.104181562775778]
  A number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time.
  arXiv  Detail & Related papers  (2020-04-10T02:00:29Z)

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.