Contactless Oxygen Monitoring with Gated Transformer

• URL: http://arxiv.org/abs/2212.03357v1
• Date: Tue, 6 Dec 2022 22:43:59 GMT
• Title: Contactless Oxygen Monitoring with Gated Transformer
• Authors: Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao and Dina Katabi
• Abstract summary: We propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal.
• Score: 34.09952889918388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.

Related papers

• Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals [1.2618555186247336]
  We propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS) We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties.
  arXiv  Detail & Related papers  (2024-09-24T14:40:09Z)
• Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters [39.96015789655091]
  In many fMRI studies, respiratory signals are often missing or of poor quality. It could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.
  arXiv  Detail & Related papers  (2024-04-30T21:53:11Z)
• Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort [55.41644538483948]
  This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries.
  arXiv  Detail & Related papers  (2023-07-03T18:06:36Z)
• Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing [0.1759252234439348]
  Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. This study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Time-series respiration data were collected using infrared light-wave sensing technology.
  arXiv  Detail & Related papers  (2023-01-09T23:19:40Z)
• PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation [54.839600943189915]
  Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks.
  arXiv  Detail & Related papers  (2022-12-14T21:39:15Z)
• Minute ventilation measurement using Plethysmographic Imaging and lighting parameters [8.739176372427842]
  Breathing disorders such as sleep apnea is a critical disorder that affects a large number of individuals due to the insufficient capacity of the lungs to contain/exchange oxygen and carbon dioxide to ensure that the body is in the stable state of homeostasis. Respiratory Measurements such as minute ventilation can be used in correlation with other physiological measurements such as heart rate and heart rate variability for remote monitoring of health and detecting symptoms of such breathing related disorders.
  arXiv  Detail & Related papers  (2022-08-29T00:42:48Z)
• fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits [62.997667081978825]
  We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI) The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session.
  arXiv  Detail & Related papers  (2021-12-21T06:52:48Z)
• Dual Attention Network for Heart Rate and Respiratory Rate Estimation [1.5469452301122175]
  Non-contact camera based physiological measurement is more accessible and convenient in Telehealth nowadays. It's also desirable to have a unified network which could estimate both heart rate and respiratory rate to reduce system complexity and latency. We propose a convolutional neural network which leverages spatial attention and channel attention, which we call it dual attention network (DAN) to jointly estimate heart rate and respiratory rate with camera video as input.
  arXiv  Detail & Related papers  (2021-10-31T03:00:28Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• 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.