Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

• URL: http://arxiv.org/abs/2004.09834v1
• Date: Tue, 21 Apr 2020 09:07:09 GMT
• Title: Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea
• Authors: Gaetano Scebba, Giulia Da Poian, and Walter Karlen
• Abstract summary: Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. We present a novel algorithm based on multispectral data fusion that aims at estimating respiratory rate (RR) during apnea. Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.
• Score: 7.300192965401497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

Related papers

• Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment [45.104212062055424]
  This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), were trained to predict breathing types.
  arXiv  Detail & Related papers  (2024-09-05T02:14:31Z)
• Pre-Trained Foundation Model representations to uncover Breathing patterns in Speech [2.935056044470713]
  Respiratory rate (RR) is a vital metric that is used to assess the overall health, fitness, and general well-being of an individual. Existing approaches to measure RR are performed using specialized equipment or training. Studies have demonstrated that machine learning algorithms can be used to estimate RR using bio-sensor signals as input.
  arXiv  Detail & Related papers  (2024-07-17T21:57:18Z)
• 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)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• 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)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation [1.2291501047353484]
  There is a need in general medicine and critical care for a more convenient method to automatically detect sleep apnea from a simple, easy-to-wear device. The objective is to automatically detect abnormal respiration and estimate the Apnea-Hypopnea-Index (AHI) with a wearable respiratory device. Four models were trained: one each using the respiratory features only, a feature from the SpO2 (%)-signal only, and two additional models that use the respiratory features and the SpO2 (%)-feature.
  arXiv  Detail & Related papers  (2021-02-24T02:04:57Z)
• Automated Respiratory Event Detection Using Deep Neural Networks [3.489191364043618]
  We train a neural network based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Our fully automated method can detect respiratory events and assess the apnea-hypopnea index with sufficient accuracy for clinical utilization.
  arXiv  Detail & Related papers  (2021-01-12T17:43:17Z)
• Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks [68.8204255655161]
  We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
  arXiv  Detail & Related papers  (2020-12-29T01:14:17Z)
• Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms [10.232449356645608]
  Non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. frequent contact between users and the cloud might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency.
  arXiv  Detail & Related papers  (2020-11-17T07:33:00Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)

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.