Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases.

Publication date: Jun 15, 2023

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88. 79% (without Stage 1 (data size reducing convolutional layer)), 90. 58% (with 1 cD7 3 Stage 1), and 91. 77% (with 1 cD7 5 Stage 1).

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Concepts Keywords
Cnn Communicable Diseases
Hemodynamic convolutional neural network
Pandemic COVID-19
Smartwatches COVID-19
Stage Deep Learning
deep learning
Neural Networks, Computer
respiratory disease
wearable device


Type Source Name
disease VO time
disease MESH Infectious Diseases
disease MESH COVID-19
disease MESH respiratory diseases
disease IDO algorithm
disease VO device
disease VO USA
disease VO Canada
drug DRUGBANK Coenzyme M
disease VO Viruses
disease VO Fungi
disease VO Bacteria
disease MESH tuberculosis
pathway KEGG Tuberculosis
disease MESH diphtheria
disease MESH bacterial pneumonia
disease MESH viral pneumonia
disease MESH influenza
disease VO organization
pathway REACTOME Infectious disease
disease IDO infectious disease
disease VO effective
disease IDO blood
disease MESH infection
disease VO efficient
disease MESH respiratory infections
drug DRUGBANK Oxygen
disease MESH critically ill
disease VO nose
disease MESH psychological stress
disease MESH Tachypnea
disease MESH Central Apnea
disease VO protocol
disease VO mouth
disease MESH pneumonia
drug DRUGBANK Flunarizine
disease MESH sti
disease IDO process
drug DRUGBANK Medical air
disease VO volume
disease MESH death
disease IDO contact tracing
drug DRUGBANK Efavirenz
disease MESH Long COVID
disease MESH cardiac arrest
drug DRUGBANK Guanosine
disease IDO object
disease MESH lung diseases
disease VO Pla
disease MESH schizophrenia
disease MESH Methemoglobinemia
disease MESH Etiology

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