Spirometry-based airways disease simulation and recognition using
Machine Learning approaches
- URL: http://arxiv.org/abs/2111.04315v1
- Date: Mon, 8 Nov 2021 08:01:18 GMT
- Title: Spirometry-based airways disease simulation and recognition using
Machine Learning approaches
- Authors: Riccardo Dio (AROMATH, UCA), Andr\'e Galligo (AROMATH, UCA), Angelos
Mantzaflaris (AROMATH, UCA), Benjamin Mauroy (UCA)
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The purpose of this study is to provide means to physicians for automated and
fast recognition of airways diseases. In this work, we mainly focus 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.
This allows us to simulate ventilation under the hypothesis of ventilation at
rest (tidal breathing). By changing the resistive and elastic parameters, data
samples are realized simulating healthy, fibrosis and asthma breathing. On this
synthetic data, different machine learning models are tested and their
performance is assessed. All but the Naive bias classifier show accuracy of at
least 99%. This represents a proof of concept that Machine Learning can
accurately differentiate diseases based on manufactured spirometry data. This
paves the way for further developments on the topic, notably testing the model
on real data.
Related papers
- Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes [3.2458203725405976]
This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease classification and biometric analysis.
By leveraging one of the largest publicly available medical database of respiratory sounds, we train machine learning models to classify various respiratory health conditions.
Our approach achieves high accuracy in both binary classification (89% balanced accuracy for healthy vs. diseased) and multi-class classification (72% balanced accuracy for specific diseases like pneumonia and COPD)
arXiv Detail & Related papers (2023-09-12T23:54:00Z) - 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) - An Adversarial Active Sampling-based Data Augmentation Framework for
Manufacturable Chip Design [55.62660894625669]
Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable.
Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks.
We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
arXiv Detail & Related papers (2022-10-27T20:53:39Z) - BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot
Detection [63.447493500066045]
This work proposes a data driven learning model for the synthesis of keystroke biometric data.
The proposed method is compared with two statistical approaches based on Universal and User-dependent models.
Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
arXiv Detail & Related papers (2022-07-27T09:26:15Z) - An Apparatus for the Simulation of Breathing Disorders: Physically
Meaningful Generation of Surrogate Data [24.50116388903113]
We introduce an apparatus comprising of PVC tubes and 3D printed parts as a simple yet effective method of simulating both obstructive and restrictive respiratory waveforms in healthy subjects.
Independent control over both inspiratory and expiratory resistances allows for the simulation of obstructive breathing disorders through the whole spectrum of FEV1/FVC spirometry ratios.
waveform characteristics of breathing disorders, such as a change in inspiratory duty cycle or peak flow are also observed in the waveforms resulting from use of the artificial breathing disorder simulation apparatus.
arXiv Detail & Related papers (2021-09-14T14:00:37Z) - Simulated Adversarial Testing of Face Recognition Models [53.10078734154151]
We propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner.
We are the first to show that weaknesses of models trained on real data can be discovered using simulated samples.
arXiv Detail & Related papers (2021-06-08T17:58:10Z) - A Model-Based Approach to Synthetic Data Set Generation for
Patient-Ventilator Waveforms for Machine Learning and Educational Use [0.0]
We propose a model-based approach to generate a synthetic data set for machine learning and educational use.
We generated a synthetic data set using 9 different patient archetypes, which are derived from measurements in the literature.
arXiv Detail & Related papers (2021-03-29T15:10:17Z) - Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype
Prediction [55.94378672172967]
We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients.
We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks.
Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
arXiv Detail & Related papers (2020-09-02T02:50:30Z) - Automatic Recall Machines: Internal Replay, Continual Learning and the
Brain [104.38824285741248]
Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.
We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective.
Instead the implicit memory of learned samples within the assessed model itself is exploited.
arXiv Detail & Related papers (2020-06-22T15:07:06Z) - Self-Training with Improved Regularization for Sample-Efficient Chest
X-Ray Classification [80.00316465793702]
We present a deep learning framework that enables robust modeling in challenging scenarios.
Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
arXiv Detail & Related papers (2020-05-03T02:36:00Z) - Detection of Coronavirus (COVID-19) Associated Pneumonia based on
Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model
using Chest X-ray Dataset [4.664495510551646]
This paper presents a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset.
The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia.
arXiv Detail & Related papers (2020-04-02T08:14:37Z)
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.