Article
Engineering, Electrical & Electronic
Mayuko Nakagawa, Kosuke Oiwa, Yasushi Nanai, Kent Nagumo, Akio Nozawa
Summary: The study focuses on remote blood glucose estimation using multiwavelength facial imaging. Features related to blood glucose variation were extracted from visible, near-infrared, and infrared facial images, and their accuracy in estimating blood glucose levels was evaluated. The results showed that near-infrared images had higher accuracy in estimating blood glucose levels. Further research suggests increasing the amount of data and searching for specific wavelength bands suitable for blood glucose level monitoring to improve the accuracy of this method.
IEEE SENSORS JOURNAL
(2023)
Article
Multidisciplinary Sciences
Asiye Sahin, Ahmet Aydin
Summary: The study utilized continuous glucose monitoring devices and artificial intelligence methods to predict advanced time blood glucose levels, aiding diabetic patients in managing their blood glucose levels effectively and providing early warnings of abnormal conditions. By training an artificial neural network, a model was developed for real-time predictions with promising results.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Automation & Control Systems
Renhong Hu, Jie Mei, Guangfu Ma
Summary: This paper investigates the delay margin problem of a linear system with multiple delays and provides estimations for allowable delay bounds in the form of matrix measure. By using weighted matrix measure and optimization technologies, the conservatism of estimated delay bounds is reduced. Numerical examples demonstrate that the weighted matrix measure performs better in estimating the delay margin of the system compared to usual matrix measures.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Health Care Sciences & Services
Jong-Uk Park, Yeewoong Kim, Yerin Lee, Erdenebayar Urtnasan, Kyoung-Joung Lee
Summary: This study proposes an algorithm that predicts hypoglycemic events using glucose levels and electrocardiogram data. The results show that the algorithm performs better than previous studies in predicting hypoglycemia, and has high sensitivity, specificity, and accuracy.
JOURNAL OF MEDICAL SYSTEMS
(2022)
Review
Health Care Sciences & Services
Arfan Ahmed, Sarah Aziz, Alaa Abd-alrazaq, Faisal Farooq, Mowafa Househ, Javaid Sheikh
Summary: This study provides a systematic review and quality assessment of wearable devices using artificial intelligence for forecasting or predicting blood glucose levels in patients with diabetes. The results indicate that although there are few current studies, the quality of these studies is high, and wearable devices have the potential to replace invasive devices for glucose monitoring in the future.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Chemistry, Analytical
Subhadip Chakraborty, Rajib Saha, Anupam Karmakar, Sanatan Chattopadhyay
Summary: This study focuses on the fabrication and characterization of novel two-electrode capacitive biosensors based on ZnO nanowires on flexible PET substrates for accurate estimation of glucose, analyzing the dielectric properties of the sample. The morphology and crystalline quality of the nanowires were analyzed using FESEM and XRD. The study also evaluated the analytical performance of the devices in terms of enzyme activity, reliability and flexibility.
Article
Computer Science, Interdisciplinary Applications
Wonju Seo, Sung-Woon Park, Namho Kim, Sang-Man Jin, Sung-Min Park
Summary: This study developed a personalized predictive model for blood glucose levels using a convolutional neural network with a fine-tuning strategy, showing significant improvements in prediction accuracy. Testing on different types of diabetes patients revealed that the fine-tuned CNN model performed excellently in various performance metrics compared to other models.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Engineering, Electrical & Electronic
Zhenyi Ye, Jie Wang, Hao Hua, Xiangdong Zhou, Qiliang Li
Summary: This study developed a non-invasive and accurate method to predict blood glucose levels using an E-Nose system and machine learning models, providing a convenient glucose monitoring tool for patients with diabetes.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Hoda Nemat, Heydar Khadem, Mohammad R. Eissa, Jackie Elliott, Mohammed Benaissa
Summary: This paper proposes advanced machine learning architectures leveraging deep learning and ensemble learning for predicting blood glucose levels. The developed ensemble models, using novel meta-learning approaches, show superior performance compared to benchmark non-ensemble models, demonstrating the efficacy of the proposed meta-learning approaches.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Multidisciplinary Sciences
Jeroen C. J. Koelemeij, Han Dun, Cherif E. V. Diouf, Erik F. Dierikx, Gerard J. M. Janssen, Christian C. J. M. Tiberius
Summary: Global navigation satellite systems (GNSS) have widespread applications in navigation and time distribution, but are affected by issues such as multipath propagation and obstructed view of the sky. This study presents a GNSS-independent terrestrial positioning system that achieves subnanosecond time synchronization through radio transmitters and fiber-optic network, mitigating the effects of multipath propagation and providing high-precision and reliable positioning and timing services.
Article
Engineering, Aerospace
Hyung Jun Park, Seokgoo Kim, Junyoung Lee, Nam Ho Kim, Joo-Ho Choi
Summary: This study proposes a system-level prognostics approach for the reaction wheel motor, which is widely used for advanced attitude control of satellites. By considering the motor as a system composed of multiple components, the approach estimates the health degradation and predicts failures based on motor operation data obtained during accelerated-life tests.
ADVANCES IN SPACE RESEARCH
(2023)
Article
Computer Science, Information Systems
Ibrahim Aljamaan, Ibraheem Al-Naib
Summary: This study proposed a nonlinear system identification approach to develop a mathematical model for predicting blood glucose levels of type 1 diabetes mellitus patients. By using simulation software and MATLAB code processing, the model showed strong predictive capability in validation tests.
Review
Medical Informatics
Kui Liu, Linyi Li, Yifei Ma, Jun Jiang, Zhenhua Liu, Zichen Ye, Shuang Liu, Chen Pu, Changsheng Chen, Yi Wan
Summary: In this systematic review and meta-analysis, the performance of machine learning models in predicting blood glucose levels and detecting adverse events in patients with diabetes mellitus was comprehensively assessed. The study found that neural network models performed the best in predicting blood glucose levels at different time intervals. Current machine learning models have sufficient ability to predict adverse events, but their ability to detect adverse events needs improvement.
JMIR MEDICAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Gyeongjun Kim, Jiwon Kang, Keemin Sohn
Summary: This study proposes a meta-reinforcement learning algorithm that uses a latent vector to recognize different contexts in a traffic environment, automatically classifies traffic conditions, and applies customized rewards for each context.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Biology
Hoda Nemat, Heydar Khadem, Jackie Elliott, Mohammed Benaissa
Summary: Effective control of blood glucose level is crucial in managing type 1 diabetes. Predicting future blood glucose levels by utilizing historical data of variables known to affect blood glucose, such as carbohydrate intake, injected insulin, and physical activity, is explored through causality analysis. Two approaches are proposed to leverage causality information for blood glucose level prediction.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Tomas Koutny
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2016)
Article
Computer Science, Hardware & Architecture
Ivanoe De Falco, Antonio Della Cioppa, Tomas Koutny, Michal Krcma, Umberto Scafuri, Ernesto Tarantin
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
I De Falco, A. Della Cioppa, A. Giugliano, A. Marcelli, T. Koutny, M. Krcma, U. Scafuri, E. Tarantino
APPLIED SOFT COMPUTING
(2019)
Article
Biology
Tomas Koutny, Michael Mayo
Summary: This study proposes a low-complexity and explainable blood glucose prediction method using the Intel P6 branch predictor algorithm. It compares this new method to a state-of-the-art deep learning method for blood glucose level prediction. The results show that the new method achieves comparable predictive accuracy with less computational complexity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Multidisciplinary Sciences
Tomas Koutny
Summary: Patients with diabetes often need to monitor their blood glucose levels regularly. However, the process of drawing blood samples can be painful and uncomfortable. Researchers have proposed a physiological model that calculates blood glucose levels based on measurements of interstitial fluid glucose levels using a sensor implanted in the subcutaneous tissue. This method can prolong the lifespan of the sensor and reduce discomfort for the patient, thereby improving their quality of life.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Analytical
Martin Ubl, Tomas Koutny, Antonio Della Cioppa, Ivanoe De Falco, Ernesto Tarantino, Umberto Scafuri
Summary: Diabetes is a group of diseases with high blood glucose levels. Controlling blood glucose levels using insulin pumps and controllers requires evaluation of their safety. This paper presents an evaluation method using a simulator to assess the safety of different insulin pump settings.
Proceedings Paper
Engineering, Electrical & Electronic
I De Falco, U. Scafuri, E. Tarantino, A. Della Cioppa, Tomas Koutny, Michal Krcma
2020 IEEE GLOBECOM WORKSHOPS (GC WKSHPS)
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
T. Koutny, M. Ubl, I De Falco, E. Tarantino, U. Scafuri, A. Della Cioppa, M. Krcma
2019 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC)
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Martin Ubl, Tomas Koutny
10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS
(2019)
Proceedings Paper
Computer Science, Information Systems
T. Koutny, I. De Falco, E. Tarantino, U. Scafuri, A. Della Cioppa, M. Krcma
2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
(2019)
Proceedings Paper
Engineering, Biomedical
Tomas Koutny, David Siroky
WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 3
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Tomas Koutny, Martin Ubl
9TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN-2018) / 8TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2018)
(2018)
Proceedings Paper
Engineering, Biomedical
T. Koutny
Proceedings Paper
Engineering, Biomedical
Tomas Koutny
BIOINFORMATICS AND BIOMEDICAL ENGINEERING, IWBBIO 2017, PT I
(2017)
Proceedings Paper
Computer Science, Cybernetics
Jan Strnadek, Tomas Koutny, Josef Kohout
2016 9TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTIONS (HSI)
(2016)
Article
Medicine, Research & Experimental
Tong Cheng, Zhusheng Chen, Yibin Qin, Xiang Zhu, Hongsheng Chen, Zhongling Xu, Xiaqing Ma
Summary: Morphine is commonly used and effective for pain relief, but its side effect of itching limits its clinical use. This paper discusses the potential of using esketamine to treat morphine-induced itching.
MEDICAL HYPOTHESES
(2024)
Article
Medicine, Research & Experimental
Sung Eun Lee, Eunjung Park, Ji-yun Kim, HyukHoon Kim
Summary: Hyperbaric oxygen therapy (HBOT) is a potential therapeutic modality that has been recognized for its favorable mechanisms in various diseases, including sepsis-associated encephalopathy (SAE). HBOT has neuroprotective effects through its anti-inflammatory and antiapoptotic effects as well as increased tissue oxygenation capacity. However, there are caveats and limitations in applying HBOT in sepsis.
MEDICAL HYPOTHESES
(2024)