Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing

• URL: http://arxiv.org/abs/2301.03713v4
• Date: Tue, 16 Apr 2024 16:00:09 GMT
• Title: Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing
• Abstract summary: 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.
• Score: 0.1759252234439348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup.

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)
• Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals [0.18641315013048293]
  We present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.
  arXiv  Detail & Related papers  (2023-11-02T16:23:13Z)
• 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)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Spirometry-based airways disease simulation and recognition using Machine Learning approaches [0.0]
  This study focuses on measures that can be easily recorded using a spirometer. The signals used in this framework are simulated using the linear bi-compartment model of the lungs. By changing the resistive and elastic parameters, data samples are realized simulating healthy, fibrosis and asthma breathing.
  arXiv  Detail & Related papers  (2021-11-08T08:01:18Z)
• 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)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• 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)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• 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)
• Identification of deep breath while moving forward based on multiple body regions and graph signal analysis [45.62293065676075]
  This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. In validation experiments, the proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively.
  arXiv  Detail & Related papers  (2020-10-20T08:26:50Z)
• Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea [7.300192965401497]
  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.
  arXiv  Detail & Related papers  (2020-04-21T09:07:09Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.