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An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features
Article
Subjects > Engineering
Europe University of Atlantic > Research > Scientific Production
Fundación Universitaria Internacional de Colombia > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
Abierto
Inglés
Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.
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Siddiqui, Hafeez-Ur-Rehman and Raza, Ali and Saleem, Adil Ali and Rustam, Furqan and Díez, Isabel de la Torre and Gavilanes Aray, Daniel and Lipari, Vivian and Ashraf, Imran and Dudley, Sandra
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, daniel.gavilanes@uneatlantico.es, vivian.lipari@uneatlantico.es, UNSPECIFIED, UNSPECIFIED
(2023)
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.
Diagnostics, 13 (6).
p. 1096.
ISSN 2075-4418
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Text
diagnostics-13-01096.pdf Available under License Creative Commons Attribution. Download (595kB) | Preview |
Abstract
Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | chronic obstructive pulmonary disease; machine learning; ultra wideband radar; feature engineering |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Date Deposited: | 28 Mar 2023 23:30 |
| Last Modified: | 28 Mar 2023 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/6551 |
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Functional foods have emerged as an attractive option for many consumers, given their wide-ranging and long-term benefits. The functional food market size was valued at USD 177,770 Million in 2019 and is estimated to reach USD 267,924.4 Million by 2027, registering a CAGR of 6.7% from 2021 to 2027. Various natural products/compounds exert significant functional activity, and could also added value to food products alone or in combination, provided functional activity. The use of natural compounds in preparation of functional foods is important due to its higher safety, superior organoleptic properties, and functional attributes, resulted in wider consumer acceptance. Also, the use of advanced technologies in formulation of functional foods provides a better means of utilizing natural compounds for organoleptic and functional attributes.
Maurizio Battino mail maurizio.battino@uneatlantico.es, Tarun Belwal mail , Miguel A. Prieto mail ,
Battino
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The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled “International Natural Product Sciences Taskforce” (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. Methods In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week “2021 INPST Twitter Networking Event” (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. Results and Conclusion The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.
Rajeev K. Singla mail , Ronita De mail , Thomas Efferth mail , Bruno Mezzetti mail , Md. Sahab Uddin mail , X. Sanusi mail , Fidele Ntie-Kang mail , Dongdong Wang mail , Fabien Schultz mail , Kiran R. Kharat mail , Hari Prasad Devkota mail , Maurizio Battino mail maurizio.battino@uneatlantico.es, Daniel Sur mail , Ronan Lordan mail , Sourav S Patnaik mail , Christos Tsagkaris mail , Chandragiri Siva Sai mail , Surya Kant Tripathi mail , Mihnea-Alexandru Găman mail , Mosa E.O. Ahmed mail , Elena González-Burgos mail , Smith B. Babiaka mail , Shravan Kumar Paswan mail , Joy Ifunanya Odimegwu mail , Faizan Akram mail , Jesus Simal-Gandara mail , Mágali S. Urquiza mail , Aleksei Tikhonov mail , Himel Mondal mail , Shailja Singla mail , Sara Di Lonardo mail , Eoghan J Mulholland mail , Merisa Cenanovic mail , Abdulkadir Yusif Maigoro mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Soojin Lee mail , Nikolay T. Tzvetkov mail , Anna Maria Louka mail , Pritt Verma mail , Hitesh Chopra mail , Scarlett Perez Olea mail , Johra Khan mail , José M. Alvarez Suarez mail , Xiaonan Zheng mail , Michał Tomczyk mail , Manoj Kumar Sabnani mail , Christhian Delfino Villanueva Medina mail , Garba M. Khalid mail , Hemanth Kumar Boyina mail , Milen I. Georgiev mail , Claudiu T. Supuran mail , Eduardo Sobarzo-Sánchez mail , Tai-Ping Fan mail , Valeria Pittala mail , Antoni Sureda mail , Nady Braidy mail , Gian Luigi Russo mail , Rosa Anna Vacca mail , Maciej Banach mail , Gérard Lizard mail , Amira Zarrouk mail , Sonia Hammami mail , Ilkay Erdogan Orhan mail , Bharat B. Aggarwal mail , George Perry mail , Mark JS Miller mail , Michael Heinrich mail , Anupam Bishayee mail , Anake Kijjoa mail , Nicolas Arkells mail , David Bredt mail , Michael Wink mail , Bernd l. Fiebich mail , Gangarapu Kiran mail , Andy Wai Kan Yeung mail , Girish Kumar Gupta mail , Antonello Santini mail , Massimo Lucarini mail , Alessandra Durazzo mail , Amr El-Demerdash mail , Albena T. Dinkova-Kostova mail , Alejandro Cifuentes mail , Eliana B. Souto mail , Muhammad Asim Masoom Zubair mail , Pravin Badhe mail , Javier Echeverría mail , Jarosław Olav Horbańczuk mail , Olaf K. Horbanczuk mail , Helen Sheridan mail , Sadeeq Muhammad Sheshe mail , Anna Maria Witkowska mail , Ibrahim M. Abu-Reidah mail , Muhammad Riaz mail , Hammad Ullah mail , Akolade R. Oladipupo mail , Víctor Lopez mail , Neeraj Kumar Sethiya mail , Bhupal Govinda Shrestha mail , Palaniyandi Ravanan mail , Subash Chandra Gupta mail , Qushmua E. Alzahrani mail , Preethidan Dama Sreedhar mail , Jianbo Xiao mail , Mohammad Amin Moosavi mail , Parasuraman Aiya Subramani mail , Amit Kumar Singh mail , Ananda Kumar Chettupalli mail , Jayanta Kumar Patra mail , Gopal Singh mail , Tomasz M. Karpiński mail , Fuad Al-Rimawi mail , Rambod Abiri mail , Atallah F. Ahmed mail , Davide Barreca mail , Sharad Vats mail , Said Amrani mail , Carmela Fimognari mail , Andrei Mocan mail , Lucian Hritcu mail , Prabhakar Semwal mail , Md. Shiblur Rahaman mail , Mila Emerald mail , Akinleye Stephen Akinrinde mail , Abhilasha Singh mail , Ashima Joshi mail , Tanuj Joshi mail , Shafaat Yar Khan mail , Gareeballah Osman Adam Balla mail , Aiping Lu mail , Sandeep Ramchandra Pai mail , Imen Ghzaiel mail , Niyazi Acar mail , Nour Eddine Es-Safi mail , Gokhan Zengin mail , Azazahemad A. Kureshi mail , Arvind Kumar Sharma mail , Bikash Baral mail , Neeraj Rani mail , Philippe Jeandet mail , Monica Gulati mail , Bhupinder Kapoor mail , Yugal Kishore Mohanta mail , Zahra Emam-Djomeh mail , Raphael Onuku mail , Jennifer R. Depew mail , Omar M. Atrooz mail , Bey Hing Goh mail , Jose Carlos Andrade mail , Bikramjit Konwar mail , VJ Shine mail , João Miguel Lousa Dias Ferreira mail , Jamil Ahmad mail , Vivek K. Chaturvedi mail , Krystyna Skalicka-Woźniak mail , Rohit Sharma mail , Rupesh K. Gautam mail , Sebastian Granica mail , Salvatore Parisi mail , Rishabh Kumar mail , Atanas G. Atanasov mail , Bairong Shen mail ,
Singla
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El objetivo del presente estudio es comparar el perfil de los estados de ánimo (EA) en jóvenes escolares que practican diferentes deportes extraescolares de manera federada respecto a escolares de Educación Primaria y Secundaria que no están federados. Seleccionándose un total de 329 sujetos (141 deportistas y 188 escolares no practicantes). Los EA se evaluaron mediante el cuestionario Profile of Moods States (POMS). La comparación se realizó en base al deporte practicado y en función de si eran deportistas federados o no federados. Los resultados muestran valores más elevados en la escala del vigor, así como diferencias significativas en las escalas depresión y fatiga entre los deportistas. Además, se observan diferencias entre no federados escolares para la depresión, hostilidad y tensión. Se concluye que los deportistas muestran valores que se asocian con el denominado perfil iceberg.
Javier Fernández García mail javier.fernandez@uneatlantico.es, Fernando Calahorro Cañada mail , Gema Torres Luque mail ,
Fernández García
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Background: Celiac disease (CD) is a multifactorial, immune-mediated enteropathic disorder that may occur at any age with heterogeneous clinical presentation. In the last years, unusual manifestations have become very frequent, and currently, it is not so uncommon to diagnose CD in subjects with overweight or obesity, especially in adults; however, little is known in the pediatric population. This systematic review aims to evaluate the literature regarding the association between CD and overweight/obesity in school-age children. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. An electronic database search of articles published in the last 20 years in English was carried out in Web of Sciences, PubMed, and Medline. The quality of the included studies was assessed by using the STrengthening the Reporting of OBservational studies in Epidemiology statement. Results: Of the 1396 articles identified, 9 articles, investigating overweight/obesity in children/adolescents affected by CD or screening CD in children/adolescents with overweight/obesity, met the inclusion criteria. Overall, the results showed that the prevalence of overweight or obesity in school-age children (6–17 years) affected by CD ranged between 3.5% and 20%, highlighting that the coexistence of CD with overweight/obesity in children is not uncommon as previously thought. Conclusion: Although CD has been historically correlated with being underweight due to malabsorption, it should be evaluated also in children with overweight and obesity, especially those who have a familiar predisposition to other autoimmune diseases and/or manifest unusual symptoms of CD.
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De Giuseppe
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Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively.
Imran Shafi mail , Muhammad Fawad Mazhar mail , Anum Fatima mail , Roberto Marcelo Álvarez mail roberto.alvarez@uneatlantico.es, Yini Airet Miró Vera mail yini.miro@uneatlantico.es, Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Imran Ashraf mail ,
Shafi