scholarly journals A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

2021 ◽  
Author(s):  
Javier Andreu-Perez ◽  
Humberto Perez-Espinosa ◽  
Eva Timonet ◽  
Mehrin Kiani ◽  
Manuel I. Girón-Pérez ◽  
...  
Keyword(s):  
Pcr Analysis ◽  
Web Tool ◽  
A Algorithm ◽  
Audio Features ◽  
Mode Decomposition ◽  
Artificial Neural Network Classifier ◽  
Severity Levels ◽  
Web App ◽  
Analysis System ◽  
Cough Sound

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative). Samples were clinically labelled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold and lymphocytes count from the patients. Our proposed generic method is a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world.

A microcomputer-based interactive cough sound analysis system

1991 ◽  
Vol 36 (1) ◽  
pp. 33-43 ◽  
Author(s):  
C. William Thorpe ◽  
W. Richard Fright ◽  
Leslie J. Toop ◽  
Kenneth P. Dawson
Keyword(s):  
Sound Analysis ◽  
Analysis System ◽  
Cough Sound

scholarly journals OBACIS Phase II: Catalogs and Auto-Generated Course Information Sheets

2018 ◽  
Author(s):  
Mohamed A. Ismail
Keyword(s):  
Continuous Improvement ◽  
Program Assessment ◽  
Data Driven ◽  
Web Tool ◽  
Creation Process ◽  
Order Of Magnitude ◽  
Original Goal ◽  
Web App ◽  
Previous Visit ◽  
The Web

 Abstract—OBACIS is an integrated framework being developed to accelerate the accreditation reporting workflow, cut down the reporting cost by an order of magnitude, and close the data-driven continuous improvement loop. The framework integrates three different pieces of software: 1) an Excel Add-in, or the "Xl-App", for simultaneous grade and OBA reporting; 2) A Windows Application, or the "Win-App" for program and faculty-level template creation, document compilation, and program assessment; and, 3) a web-tool, or the "Web-App", for document compilation and reporting. This paper focuses on creating a centralized database for compiling raw data related to accreditation reporting from various resources such as previous visit accreditation reports, academic calendars, course schedules, and a handful of other resources are used to create what we call OBACIS Catalogs. The Catalogs framework is a part of the bigger OBACIS framework proposed in CEEA 2016 [1]. The framework has been implemented as a module of the Win-App. Automating the creation process of Course Information Sheets (CIS) was the original goal and is still one of the main outputs of the proposed framework. The OBACIS Catalogs are supposed to save a sheer amount of time needed for accreditation reporting and should act as an instrumental tool for accelerating accreditation data collection, creating insightful analyses, and identifying gaps for continuous improvement initiatives at both program and faculty levels.

scholarly journals Development of a network visualization and analysis system for malignant tumors based on transcriptome data

2021 ◽  
Vol 271 ◽  
pp. 03077
Author(s):  
Geng Zezheng ◽  
Fang Xiaonan ◽  
Bi Chuanmei ◽  
Dong Ling
Keyword(s):  
Decision Support ◽  
Malignant Tumors ◽  
Clinical Decision ◽  
R Package ◽  
Network Visualization ◽  
Transcriptome Data ◽  
Network Diagram ◽  
Cerna Network ◽  
Web App ◽  
Analysis System

Shiny technology has developed rapidly in recent years, as an R package for developing interactive app, through which we can package the written R code into a web app, which can not only save user time, but also accelerate the development of the speed of user-end communication, analyze the transcriptome data of related malignant tumors, and construct a ceRNA network diagram of desired malignant tumors. The code utilizing shiny technology package can facilitate users to map the ceRNA network associated with malignant tumors only through screen operation, significantly improving the efficiency and accuracy of clinical decision support in primary hospitals.

scholarly journals A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

2021 ◽  
pp. 1-1
Author(s):  
Javier Andreu-Perez ◽  
Humberto Perez-Espinosa ◽  
Eva Timonet ◽  
Mehrin Kiani ◽  
Manuel Ivan Giron-Perez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Severity Levels ◽  
Analysis System

scholarly journals AFCMHeatMap: A shiny web tool for heatmap generation of genetic expression datasets using R packages

2017 ◽  
Author(s):  
Mohammad Tarek ◽  
Ayman S Shafei ◽  
Mahmoud A Ali
Keyword(s):  
Expression Data ◽  
Rna Seq ◽  
Web Tool ◽  
Graphical Visualization ◽  
Supplementary Material ◽  
R Packages ◽  
Web App ◽  
Visualization Of Data ◽  
The Individual ◽  
User Friendly

Generating heatmaps of genetic datasets is a 2D graphical visualization of data where the individual expression values contained in a matrix are represented as colors. Herein, we describe AFCMHeatMap a shiny web App that integrates quantitative interaction of genomics data and results from microarrays or RNA-Seq to highlight expression levels of various genetic datasets with a *.CSV input file. The application also facilitates downloading heatmaps as a supplementary material for user's publications. Written in R using Shiny framework, it is a user-friendly framework for interactive expression data visualization that can be easily deployed without any restrictions to any operating system used by any online user.

scholarly journals AFCMHeatMap: A shiny web tool for HeatMap generation of genetic expression datasets using R packages

2017 ◽  
Author(s):  
Mohammad Tarek ◽  
Ayman S Shafei ◽  
Mahmoud A Ali
Keyword(s):  
Graphical Representation ◽  
Rna Seq ◽  
Input File ◽  
Web Tool ◽  
Individual Values ◽  
Supplementary Material ◽  
R Packages ◽  
Web App ◽  
The Individual ◽  
User Friendly

Generating heatmaps of genes datasets have been known to be a graphical representation of data where the individual values contained in a matrix are represented as colors. Herein, we describe AFCMHeatMap a shiny web App that integrates quantitative interaction genomics data and results from microarrays or RNA-Seq to highlight expression levels of various genetic datasets with a .CSV input file. The application also facilitates downloading heatmaps as a supplementary material for user's publications. Written in R using Shiny framework, it is a user-friendly framework for integrative genetic analyses that can be easily deployed across various operating systems distributions.

scholarly journals AFCMHeatMap: A shiny web tool for heatmap generation of genetic expression datasets using R packages

2017 ◽  
Author(s):  
Mohammad Tarek ◽  
Ayman S Shafei ◽  
Mahmoud A Ali
Keyword(s):  
Expression Data ◽  
Rna Seq ◽  
Web Tool ◽  
Graphical Visualization ◽  
Supplementary Material ◽  
R Packages ◽  
Web App ◽  
Visualization Of Data ◽  
The Individual ◽  
User Friendly

Generating heatmaps of genetic datasets is a 2D graphical visualization of data where the individual expression values contained in a matrix are represented as colors. Herein, we describe AFCMHeatMap a shiny web App that integrates quantitative interaction of genomics data and results from microarrays or RNA-Seq to highlight expression levels of various genetic datasets with a *.CSV input file. The application also facilitates downloading heatmaps as a supplementary material for user's publications. Written in R using Shiny framework, it is a user-friendly framework for interactive expression data visualization that can be easily deployed without any restrictions to any operating system used by any online user.

scholarly journals Unreadable Segment Recognition of Single-Lead ECG Signals Based on XGBoost: Fuision of Shannon Energy Envelope and Empirical Mode Decomposition

2021 ◽  
Author(s):  
Hanshuang Xie ◽  
Jiayi Yan ◽  
Huaiyu Zhu ◽  
Qineng Cao ◽  
Yamin Liu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Error Rate ◽  
Wearable Devices ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Shannon Energy ◽  
Analysis System ◽  
Fold Cross Validation

The quality of ECG signals is commonly affected by severe noise, especially for the single-lead ECG signals acquired from long-term wearable devices. Recognizing and ignoring these interfered signals can reduce the error rate of automatic ECG analysis system, and in addition, improve the efficiency of cardiologists. Based on XGBoost classifier, we propose an unreadable ECG segment recognition method using features extracted through Shannon Energy Envelope (SEE) and Empirical Mode Decomposition (EMD). An unreadable CarePatchTM ECG patch database is established, containing 8169 readable segments and 6114 unreadable segments with a length of 10 seconds. The XGBoost with 5-fold cross-validation is applied and obtained an accuracy of 99.51+/-0.15%. In conclusion, SSE and EMD features contribute to the unreadable segments recognition and alleviate the misdiagnosis of abnormal rhythms.

Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

1977 ◽  
Vol 35 ◽  
pp. 220-221
Author(s):  
S.F. Stinson ◽  
J.C. Lilga ◽  
M.B. Sporn
Keyword(s):  
Image Analysis ◽  
Pattern Analysis ◽  
Relative Proportion ◽  
Automated Image Analysis ◽  
Image Analyzer ◽  
Cross Sectional ◽  
Exfoliated Cells ◽  
Neoplastic Lesions ◽  
Analysis System ◽  
Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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